M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD (Established by an Act No.30 of 2008 of A.P. State Legislature) Kukatpally, Hyderabad – 500 085, Andhra Pradesh (India) M. TECH. IMAGE PROCESSING (R13) COURSE STRUCTURE AND SYLLABUS I Year I Semester Code Group
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Subject Advanced Data Structures and Algorithms Digital Image Processing Digital Signal Processing Advanced Computer Graphics ELECTIVE – I Pattern Recognition Speech Processing Machine Learning ELECTIVE –II Wireless Networks & Mobile Computing Embedded Systems Video Processing Programming and Computer Graphics Lab Seminar Total Subject Remote Sensing & GIS Neural Networks Robotics and Machine Vision Parallel Algorithms ELECTIVE –III Biometrics Data Mining Radar Imaging ELECTIVE –IV Genetic Algorithms Storage Area Networks Multimedia Databases Image Processing Lab using MATLAB Seminar Total
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I Year -II Semester Code Group
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Elective -IV
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II Year - I Semester Code Group
II Year II Semester Code Group
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) ADVANCED DATA STRUCTURES AND ALGORITHMS Objectives:
The fundamental design, analysis, and implementation of basic data structures.
Basic concepts in the specification and analysis of programs. Principles for good program design, especially the uses of data abstraction. Significance of algorithms in the computer field Various aspects of algorithm development Qualities of a good solution
UNIT I Algorithms, Performance analysis- time complexity and space complexity, Asymptotic Notation-Big Oh, Omega and Theta notations, Complexity Analysis Examples. Data structures-Linear and non linear data structures, ADT concept, Linear List ADT, Array representation, Linked representation, Vector representation, singly linked lists -insertion, deletion, search operations, doubly linked lists-insertion, deletion operations, circular lists. Representation of single, two dimensional arrays, Sparse matrices and their representation.
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UNIT II Stack and Queue ADTs, array and linked list representations, infix to postfix conversion using stack, implementation of recursion, Circular queue-insertion and deletion, Dequeue ADT, array and linked list representations, Priority queue ADT, implementation using Heaps, Insertion into a Max Heap, Deletion from a Max Heap, java.util package-ArrayList, Linked List, Vector classes, Stacks and Queues in java.util, Iterators in java.util.
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UNIT III Searching–Linear and binary search methods, Hashing-Hash functions, Collision Resolution methods-Open Addressing, Chaining, Hashing in java.util-HashMap, HashSet, Hashtable. Sorting –Bubble sort, Insertion sort, Quick sort, Merge sort, Heap sort, Radix sort, comparison of sorting methods.
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UNIT IV Trees- Ordinary and Binary trees terminology, Properties of Binary trees, Binary tree ADT, representations, recursive and non recursive traversals, Java code for traversals, Threaded binary trees. Graphs- Graphs terminology, Graph ADT, representations, graph traversals/search methods-dfs and bfs, Java code for graph traversals, Applications of Graphs-Minimum cost spanning tree using Kruskal’s algorithm, Dijkstra’s algorithm for Single Source Shortest Path Problem. UNIT V Search trees- Binary search tree-Binary search tree ADT, insertion, deletion and searching operations, Balanced search trees, AVL trees-Definition and examples only, Red Black trees – Definition and examples only, B-Trees-definition, insertion and searching operations, Trees in java.util- TreeSet, Tree Map Classes, Tries(examples only),Comparison of Search trees. Text compression-Huffman coding and decoding, Pattern matching-KMP algorithm. TEXT BOOKS: 1. 2. 3.
Data structures, Algorithms and Applications in Java, S.Sahni, Universities Press. rd Data structures and Algorithms in Java, Adam Drozdek, 3 edition, Cengage Learning. Data structures and Algorithm Analysis in Java, M.A.Weiss, 2nd edition, Addison-Wesley (Pearson Education).
REFERENCE BOOKS: 1. 2.
Java for Programmers, Deitel and Deitel, Pearson education. Data structures and Algorithms in Java, R.Lafore, Pearson education.
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3. Java: The Complete Reference, 8 editon, Herbert Schildt, TMH. rd 4. Data structures and Algorithms in Java, M.T.Goodrich, R.Tomassia, 3 edition, Wiley India Edition. 5. Data structures and the Java Collection Frame work,W.J.Collins, Mc Graw Hill. 6. Classic Data structures in Java, T.Budd, Addison-Wesley (Pearson Education). 7. Data structures with Java, Ford and Topp, Pearson Education. 8. Data structures using Java, D.S.Malik and P.S.Nair, Cengage learning. 9. Data structures with Java, J.R.Hubbard and A.Huray, PHI Pvt. Ltd. 10. Data structures and Software Development in an Object-Oriented Domain, J.P.Tremblay and G.A.Cheston, Java edition, Pearson Education.
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) DIGITAL IMAGE PROCESSING Objectives: To learn the fundamentals of digital image processing and algorithms. To understand transformations and spatial operations in digital image processing. To implement basic image processing algorithms. UNIT I Fundamental steps of image processing, components of an image processing of system, the image model and image acquisition, sampling and quantization, station ship between pixels, distance functions, scanner. UNIT II Statistical and spatial operations, Grey level transformations, histogram equalization, smoothing & sharpening-spatial filters, frequency domain filters, homomorphic filtering, image filtering & restoration. Inverse and weiner filtering. FIR weiner filter. Filtering using image transforms, smoothing splines and interpolation.
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UNIT III Morphological and other area operations, basic morphological operations, opening and closing operations, dilation erosion, Hit or Miss transform, morphological algorithms, extension to grey scale images. Segmentation and Edge detection region operations, basic edge detection, second order detection, crack edge detection, gradient operators, compass and laplace operators, edge linking and boundary detection, thresholding, region based segmentation, segmentation by morphological watersheds.
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UNIT IV Image compression: Types and requirements, statistical compression, spatial compression, contour coding, quantizing compression, image data compression-predictive technique, pixel coding, transfer coding theory, lossy and lossless predictive type coding. Basics of color image processing, pseudocolor image processing, color transformation, color smoothing and sharpening, color segmentation, color image compression, compression standards.
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UNIT V Image Transforms - Fourier, DFT, DCT, DST, Haar, Hotelling, Karhunen -Loeve, Walsh, Hadamard, Slant. Representation and Description - Chain codes, Polygonal approximation, Signatures Boundary Segments, Skeltons, Boundary Descriptors, Regional Descriptors, Relational Descriptors, PCA. TEXT BOOKS: 1. Digital Image Processing – by Rafael.C.Gonzalez & Richard E.Woods, 3 Education, 2008. 2. Digital Image Processing, M.Anji Reddy, Y.Hari Shankar, BS Publications. 3. Fundamentals of Digital Image Processing – by A.K. Jain, PHI.
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REFERENCE BOOKS: 1. Digital Image Processing – William K, Part I - John Wiley edition. 2. Digital Image Processing using MATLAB – by Rafael.C.Gonzalez, Richard E.Woods, & Steven L.Eddins, Pearson Education, 2006 3. Digital Image Processing, Kenneth R. Castleman, Pearson Education, 2007
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) DIGITAL SIGNAL PROCESSING Objectives: To understanding the digital signal processing approach and digital filter design with a computer based approach. To introduce signals, systems, time and frequency domain concepts and the associated mathematical tools that are fundamental to all DSP techniques; To provide a thorough understanding and working knowledge of design, implementation, analysis and comparison of digital filters for processing of discrete time signals. UNIT I INTRODUCTION: Introduction to Digital Signal Processing: Discrete time signals & sequences, linear shift invariant systems, stability, and causality. Linear constant coefficient difference equations. Frequency domain representation of discrete time signals and systems. DISCRETE FOURIER SERIES: Properties of discrete Fourier series, DFS representation of periodic sequences, Discrete Fourier transforms: Properties of DFT, linear convolution of sequences using DFT, Computation of DFT. Relation between Z-transform and DFS
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UNIT II FAST FOURIER TRANSFORMS: Fast Fourier transforms (FFT) - Radix-2 decimation in time and decimation in frequency FFT Algorithms, Inverse FFT, and FFT for composite N REALIZATION OF DIGITAL FILTERS: Review of Z-transforms, Applications of Z – transforms, solution of difference equations of digital filters, Block diagram representation of linear constantcoefficient difference equations, Basic structures of IIR systems, Transposed forms, Basic structures of FIR systems, System function,
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UNIT III IIR DIGITAL FILTERS: Analog filter approximations – Butter worth and Chebyshev, Design of IIR Digital filters from analog filters, Design Examples: Analog-Digital transformations FIR DIGITAL FILTERS: Characteristics of FIR Digital Filters, frequency response. Design of FIR Digital Filters using Window Techniques, Frequency Sampling technique, Comparison of IIR & FIR filters.
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UNIT IV MULTIRATE DIGITAL SIGNAL PROCESSING: Decimation, interpolation, sampling rate conversion, Implementation of sampling rate conversion. UNIT V INTRODUCTION TO DSP PROCESSORS: Introduction to programmable DSPs: Multiplier and Multiplier Accumulator (MAC), Modified Bus Structures and Memory Access schemes in DSPs Multiple access memory, multiport memory, VLSI Architecture, Pipelining, Special addressing modes, On-Chip Peripherals. Architecture of TMS 320C5X- Introduction, Bus Structure, Central Arithmetic Logic Unit, Auxiliary Registrar, Index Registrar, Auxiliary Register Compare Register, Block Move Address Register, Parallel Logic Unit, Memory mapped registers, program controller, some flags in the status registers, On- chip registers, On-chip peripherals TEXT BOOKS: 1. Digital Signal Processing, Principles, Algorithms, and Applications: John G. Proakis, Dimitris G. Manolakis, Pearson Education / PHI, 2007. 2. Discrete Time Signal Processing – A.V.Oppenheim and R.W. Schaffer, PHI 3. Digital Signal Processors – Architecture, Programming and Applications,, B.Venkataramani, M. Bhaskar, TATA McGraw Hill, 2002 REFERENCE BOOKS: 1. Digital Signal Processing: Andreas Antoniou, TATA McGraw Hill , 2006 2. Digital Signal Processing: MH Hayes, Schaum’s Outlines, TATA Mc-Graw Hill, 2007. 3. DSP Primer - C. Britton Rorabaugh, Tata McGraw Hill, 2005.
M. TECH. IMAGE PROCESSING-R13 Regulations
4. Fundamentals of Digital Signal Processing using Matlab – Robert J. Schilling, Sandra L. Harris, Thomson, 2007.
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5. Digital Signal Processing – Alan V. Oppenheim, Ronald W. Schafer, PHI Ed., 2006
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) ADVANCED COMPUTER GRAPHICS Objectives: To make students understand about fundamentals of Graphics to enable them to design animated scenes for virtual object creations. To make the student present the content graphically. UNIT I Basics, Rendering polygonal objects, Theoretical foundations, The theory and practice of light/object interaction, The theory and practice of parametric representation techniques, The theory and practice of anti-aliasing techniques.
UNIT III Ray tracing I : Basic recursive ray tracing Ray tracing II: Practical ray tracing Ray tracing III: Advanced ray tracing models
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UNIT II Advanced Rendering Techniques: Approaches, Applications and Algorithms, Shadow generation techniques, Mapping techniques: texture and environment mapping, Procedural texture mapping and modeling
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UNIT IV Radiosity methods, Global illumination models, Volume rendering techniques, Advanced rendering interfaces: shading languages and RenderMan UNIT V Advanced Animation, Overview and low-level motion specification, Animating articulated structures, Soft Object animation, Procedural animation
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1. Computer Graphics – C Version, Donald Hearn & M.Pauline Baker, Second Edition, Pearson Education,2007 2. Computer Graphics-Principles & Practice, James D.Foley, Andries van Dam, Steven K.Feiner & John F.Hughes, Second Edition in C, Pearson Education, 2007 REFERENCE BOOK:
1. Computer Graphics: a programming approach, Steven Harrington, MGH
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) PATTERN RECOGNITION (ELECTIVE-I) Objectives: To implement pattern recognition and machine learning theories To design and implement certain important pattern recognition techniques To apply the pattern recognition theories to applications of interest To implement the entropy minimization, clustering transformation and feature ordering UNIT I INTRODUCTION - Basic concepts, Applications, Fundamental problems in pattern Recognition system design, Design concepts and methodologies, Examples of Automatic Pattern recognition systems, Simple pattern recognition model DECISION AND DISTANCE FUNCTIONS - Linear and generalized decision functions, Pattern space and weight space, Geometrical properties, implementations of decision functions, Minimum-distance pattern classifications.
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UNIT II PROBABILITY - Probability of events: Random variables, Joint distributions and densities, Movements of random variables, Estimation of parameter from samples. STATISTICAL DECISION MAKING - Introduction, Baye’s theorem, Multiple features, Conditionally independent features, Decision boundaries, Unequal cost of error, estimation of error rates, the leaving-one-out-techniques, characteristic curves, estimating the composition of populations. Baye’s classifier for normal patterns.
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UNIT III NON PARAMETRIC DECISION MAKING - Introduction, histogram, kernel and window estimation, nearest neighbour classification techniques. Adaptive decision boundaries, adaptive discriminant functions, Minimum squared error discriminant functions, choosing a decision making techniques. CLUSTERING AND PARTITIONING - Hierarchical Clustering: Introduction, agglomerative clustering algorithm, the single-linkage, complete-linkage and average-linkage algorithm. Ward’s method Partition clustering-Forg’s algorithm, K-means’s algorithm, Isodata algorithm.
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UNIT IV PATTERN PREPROCESSING AND FEATURE SELECTION: Introduction, distance measures, clustering transformation and feature ordering, clustering in feature selection through entropy minimization, features selection through orthogonal expansion, binary feature selection. UNIT V SYNTACTIC PATTERN RECOGNITION & APPLICATION OF PATTERN RECOGNITION Introduction, concepts from formal language theory, formulation of syntactic pattern recognition problem, syntactic pattern description, recognition grammars, automata as pattern recognizers, Application of pattern recognition techniques in bio-metric, facial recognition, IRIS scon, Finger prints, etc., TEXT BOOKS: 1. Gose. Johnsonbaugh. Jost. “ Pattern recognition and Image Analysis”, PHI. 2. Tou. Rafael. Gonzalez. “Pattern Recognition Principle”, Pearson Education REFERENCE BOOK: 1. Richard duda, Hart., David Strok, “Pattern Classification”, John Wiley.
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) SPEECH PROCESSING (ELECTIVE-I) Objectives: To analyze a speech signal in terms of its frequency content. To understand the basics of human speech production mechanism. To understand which speech coding methods are used for what reasons. To implement LPC Analysis UNIT I INTRODUCTION Production of speech, sound perception, speech Analysis, speech coding, speech Enhancement, speech Synthesis, speech and speaker Recognition. Signals and Linear Systems: Simple signal, Filtering and convolution, Frequency Analysis: Fourier Transform, spectra and Correlation, Laplace Transform: Poles and Zeros, Discrete –Time Signal and Systems: Sampling, Frequency Transforms of Discrete-Time Signals, Decimation and Interpolation Filter: Band pass Filter, Digital Filters, Difference Equations and Interpolation
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UNIT II SPEECH PRODUCTION AND ACOUSTIC PHONETICS: Anatomy and Physiology of the speech Organs: the Lungs and the Thorax, Larynx and Vocal Folds(cords), Vocal Tract, Articulatory phonetics: Manner of Atriculatory, Structure of the Syllable, Voicing, Place of the Articulation, Phonemes in Other Language, Articulatory Models, Acoustic Phonetics : Spectrograms, Vowels, Diphthongs, glides and Liquids, Nasals, Fricatives, stops (Plosives), Variants of Normal Speech SPEECH ANALYSIS: Introduction, Short-Time speech Analysis: Windowing, Spectra of Windows: Wide-and Narrow –Band Spectrograms, Time-domain Parameters: Signal Analysis in the Time Domain, Short –Time Average Energy and Magnitude, Short –Time Average Zero-Crossing Rate ( ZCR), short-Time Autocorrelation Function , Frequency–Domain (Spectral) Parameters: Filter–Bank Analysis, Short-Time Fourier Transform Analysis, Spectral Displays, Formant Estimation and Tracking
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UNIT III LINEAR PREDICTIVE CODING (LPC) ANALYSIS: Basic Principles of LPC, Least –Squares Autocorrelation Method, Least –Squares Covariance Method, Computation Considerations, Spectral Estimation Via LPC, Updating the LPC Model Sample by Sample, Window Considerations - Cepstral Analysis: Mathematical details of Cepstral analysis, Applications for the spectrum, Mel-Scale Cepstrum, F0 Pitch estimation: Time domain F0 estimation methods, short-time Spectral methods UNIT IV Introduction to speech recognition: Variability in speech signals, segmenting speech into smaller units, Performance evaluation, Database for speech recognition, pattern recognition methods, preprocessing, parametric representation: parameters used in speech recognition, feature extraction, Evaluation of similarity of speech patterns: frame-based distance measures - HMM based Speech recognition: HMM representation, Balm-Welch re-estimation training, testing, Viterbi algorithm, speech segmentation, making ASR decisions UNIT V Speaker recognition: Introduction, Verification Vs. Recognition, Recognition techniques: Model evaluation, text dependence, statical Vs. dynamic features, stochastic models, vector quantization, similarity and distance measures, cepstral analysis, Features that distinguish the speakers: measures of the effectiveness of features, techniques to choose features, spectral features, prosodic features TEXT BOOK: 1. Speech Communication Douglas O’ Shaughnessy, Universities Press. REFERENCE BOOKS: 1. Fundamentals of Speech Recognition, Lawrence Rabiner, Biing-Hwang Juang, Pearson Edn. 2. Speech and Language processing, Daniel Jurafsky, James H. Martin, Pearson Education.
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) MACHINE LEARNING (ELECTIVE -I) Objectives: To be able to formulate machine learning problems corresponding to different applications. To understand a range of machine learning algorithms along with their strengths and weaknesses. To understand the basic theory underlying machine learning. To be able to apply machine learning algorithms to solve problems of moderate complexity. To be able to read current research papers and understands the issues raised by current research. UNIT I INTRODUCTION - Well-posed learning problems, Designing a learning system, Perspectives and issues in machine learning Concept learning and the general to specific ordering – Introduction, A concept learning task, Concept learning as search, Find-S: finding a maximally specific hypothesis, Version spaces and the candidate elimination algorithm, Remarks on version spaces and candidate elimination, Inductive bias
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UNIT II Decision Tree learning – Introduction, Decision tree representation, Appropriate problems for decision tree learning, The basic decision tree learning algorithm, Hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning Artificial Neural Networks – Introduction, Neural network representation, Appropriate problems for neural network learning, Perceptions, Multilayer networks and the back propagation algorithm, Remarks on the back propagation algorithm, An illustrative example face recognition Advanced topics in artificial neural networks Evaluation Hypotheses – Motivation, Estimation hypothesis accuracy, Basics of sampling theory, A general approach for deriving confidence intervals, Difference in error of two hypotheses, Comparing learning algorithms
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UNIT III Bayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum likelihood and least squared error hypotheses, Maximum likelihood hypotheses for predicting probabilities, Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve bayes classifier, An example learning to classify text, Bayesian belief networks The EM algorithm Computational learning theory – Introduction, Probability learning an approximately correct hypothesis, Sample complexity for Finite Hypothesis Space, Sample Complexity for infinite Hypothesis Spaces, The mistake bound model of learning - Instance-Based Learning- Introduction, k -Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Remarks on Lazy and Eager Learning Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms UNIT IV Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First Order Rules, Learning Sets of First Order Rules: FOIL, Induction as Inverted Deduction, Inverting Resolution Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge UNIT V Combining Inductive and Analytical Learning – Motivation, Inductive-Analytical Approaches to Learning, Using Prior Knowledge to Initialize the Hypothesis, Using Prior Knowledge to Alter the Search Objective, Using Prior Knowledge to Augment Search Operators, Reinforcement Learning – Introduction, The Learning Task, Q Learning, Non-Deterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming
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TEXT BOOKS: 1. Machine Learning – Tom M. Mitchell, - MGH 2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC) REFERENCES BOOKS:
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1. Cover, T. M. and J. A. Thomas: Elements of Information Theory. Wiley. 2. Charniak, E.: Statistical Language Learning. The MIT Press. 3. Jelinek, F.: Statistical Methods for Speech Recognition. The MIT Press. 4. Lutz and Ascher - "Learning Python", O'Reilly
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) WIRELESS NETWORKS & MOBILE COMPUTING (ELECTIVE-II) Objectives: The main objective of this course is to provide the students with the competences required for understanding and using the communications component of an universal communications environment. Students will be provided, in particular, with the knowledge required to understand emerging communications networks, their computational demands, the classes of distributed services and applications enabled by these networks, and the computational means required to create the new networks and the new applications.
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UNIT I WIRELESS NETWORKS: Wireless Network, Wireless Network Architecture, Wireless Switching Technology, Wireless Communication problem, Wireless Network Reference Model, Wireless Networking Issues & Standards. MOBILE COMPUTING: Mobile communication, Mobile computing, Mobile Computing Architecture, Mobile Devices, Mobile System Networks, Mobility Management
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UNIT II WIRELESS LAN: Infra red Vs radio transmission, Infrastructure and Ad-hoc Network, IEEE 802.11: System Architecture, Protocol Architecture, 802.11b, 802.11a, Newer Developments, HIPERLAN 1, HIPERLAN 2, Bluetooth : User Scenarios, Architecture. UNIT III GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS (GSM): Mobile Services, System Architecture, Protocols, Localization & Calling, Handover, Security. GPRS: GPRS System Architecture, UMTS: UMTS System Architecture. LTE: Long Term Evolution
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UNIT IV MOBILE NETWORK LAYER: Mobile IP: Goals, Assumptions, Entities and Terminology, IP Packet Delivery, Agent Discovery, Registration, Tunneling and Encapsulation, Optimizations, Dynamic Host Configuration Protocol (DHCP)
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UNIT V MOBILE TRANSPORT LAYER: Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, Fast retransmit/fast recovery, Transmission /time-out freezing, Selective retransmission, Transaction oriented TCP, TCP over 2.5G/3G Wireless Networks. TEXT BOOKS: 1. Jochen Schiller, “Mobile Communications”, Pearson Education, Second Edition, 2008. 2. Dr. Sunilkumar, et al “Wireless and Mobile Networks: Concepts and Protocols”, Wiley India. 3. Raj Kamal, “Mobile Computing”, OXFORD UNIVERSITY PRESS. REFERENCES: 1. 2. 3. 4. 5.
Asoke K Talukder, et al, “Mobile Computing”, Tata McGraw Hill, 2008. Matthew S.Gast, “802.11 Wireless Networks”, SPD O’REILLY. Ivan Stojmenovic , “Handbook of Wireless Networks and Mobile Computing”, Wiley, 2007. Kumkum Garg, “Mobile Computing”, Pearson. Handbook of Security of Networks, Yang Xiao, Frank H Li, Hui Chen, World Scientific, 2011.
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) EMBEDDED SYSTEMS (ELECTIVE –II) Objectives: design embedded computer system hardware design, implement, and debug multi-threaded application software that operates under realtime constraints on embedded computer systems use and describe the implementation of a real-time operating system on an embedded computer system formulate an embedded computer system design problem including multiple constraints, create a design that satisfies the constraints, *implement the design in hardware and software, and measure performance against the design constraints create computer software and hardware implementations that operate according to wellknown standards organize and write design documents and project reports organize and make technical presentations that describe a design.
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UNIT I Introduction to Embedded Systems: Embedded Systems, Processor Embedded into a System, Embedded Hardware Units and Devices in a System, Embedded Software, Complex System Design, Design Process in Embedded System, Formalization of System Design, Classification of Embedded Systems
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UNIT II 8051 and Advanced Processor Architecture : 8051 Architecture, 8051 Micro controller Hardware, Input/Output Ports and Circuits, External Memory, Counter and Timers, Serial data Input/Output, Interrupts, Introduction to Advanced Architectures, Real World Interfacing, Processor and Memory organization Devices and Communication Buses for Devices Network: Serial and parallel Devices & ports, Wireless Devices, Timer and Counting Devices, Watchdog Timer, Real Time Clock, Networked Embedded Systems, Internet Enabled Systems, Wireless and Mobile System protocols
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UNIT III Embedded Programming Concepts: Software programming in Assembly language and High Level Language, Data types, Structures, Modifiers, Loops and Pointers, Macros and Functions, object oriented Programming, Embedded Programming in C++ & JAVA UNIT-IV Real – Time Operating Systems : OS Services, Process and Memory Management, Real – Time Operating Systems, Basic Design Using an RTOS, Task Scheduling Models, Interrupt Latency, Response of Task as Performance Metrics RTOS Programming : Basic functions and Types of RTOSES, RTOS Vx Works, Windows CE UNIT V Embedded Software Development Process and Tools: Introduction to Embedded Software Development Process and Tools, Host and Target Machines, Linking and Locating Software, Getting Embedded Software into the Target System, Issues in Hardware-Software Design and Co-Design TEXT BOOK: 1. Embedded Systems, Raj Kamal, Second Edition TMH. REFERENCE BOOKS: 1. Embedded/Real-Time Systems, Dr.K.V.K.K.Prasad, dreamTech press 2. The 8051 Microcontroller and Embedded Systems, Muhammad Ali Mazidi, Pearson. 3. Embedded Systems, Shibu K V, Mc Graw Hill. 4. An Embedded Software Primer, David E. Simon, Pearson Education. 5. Micro Controllers, Ajay V Deshmukhi, TMH. 6. Microcontrollers, Raj kamal, Pearson Education.
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) VIDEO PROCESSING (ELECTIVE-II) Objectives: To convey details of a range of commonly used speech feature extraction techniques. To provide a basic understanding of multidimensional techniques for speech Representation, classification methods. UNIT I: PERCEPTION AND REPRESENTATION colour perception and specification. Video capture and display, Analog video raster, Analog colour TV systems, Digital Video VIDEO SAMPLING Basics of lattice theory, sampling over lattice, sampling of video signals, filtering operations, conversion of signals sampled on different lattices, sampling rate conversion of video signals.
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UNIT II: VIDEO MODELLING Camera model, illumination model, object model, scene model. Two dimensional motion models 2-D MOTION ESTIMATION: Optical flow, General methodologies, Pixel based motion estimation, Block matching algorithm, Meshbased motion estimation.
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UNIT III Global motion estimation, Region based motion estimation, Multi resolution motion estimation. Application of motion estimation in video coding. VIDEO CODING (12.5%) Information theory, Binary encoding, scalar quantization, vector quantization, Waveform based video coding: Block based transform coding, Predictive coding.
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UNIT IV: CONTENT-DEPENDENT VIDEO CODING: Two dimensional shape coding, Texture coding for arbitrarily shaped regions. Joint shape and texture coding region, object and knowledge based video coding. Semantic video coding and layered coding system. UNIT V: SCABABLE VIDEO CODING Basic mode of scabability. Object based scabability, wavelet transform based coding
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TEXT BOOKS: 1. Video processing and communication – Yao Wang, Joern Ostermann and Ya-Qin Zhang. Prentice Hall. 2. Digital video processing – M.Tekalp REFERENCE BOOKS: 1. Video Processing and Communication by Wang from Pearson education, 2002. 2. Digital Video processing by Tekalp from Pearson, 1996
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – I Sem. (Image Processing) PROGRAMMING AND COMPUTER GRAPHICS LAB
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Objectives: To understanding of critical and aesthetic issues in computer graphics and mixed-media. To know basic aesthetic principles and concepts, and the production process. To use technical, conceptual and critical abilities and appropriate technology tools. To critically evaluate computer graphics and the mixed media. i) PROGRAMMING (JAVA) 1 a) Write a Java program that prints all real solutions to the quadratic equation ax2 + bx + c = 0. Read in a, b, c and use the quadratic formula. If the discriminant b2 -4ac is negative, display a message stating that there are no real solutions. b) The Fibonacci sequence is defined by the following rule: The first two values in the sequence are 1 and 1. Every subsequent value is the sum of the two values preceding it. Write a Java program that uses both recursive and non recursive functions to print the nth value in the Fibonacci sequence. 2. a) Write a Java program that prompts the user for an integer and then prints out all prime numbers up to that integer. b) Write a Java program to multiply two given matrices. c) Write a Java Program that reads a line of integers, and then displays each integer, and the sum of all the integers (Use String Tokenizer class of java.util) 3. Write a Java program to find both the largest and smallest number in a list of integers. 4. Write a Java program to illustrate method overloading. 5. Write a Java program that implements the Sieve of Eratosthenes to find prime numbers. 6. Write a Java program to sort a list of names in ascending order. 7. Write a Java program to implement the matrix ADT using a class. The operations supported by this ADT are: a) Reading a matrix. c) Addition of matrices. b) Printing a matrix. d) Subtraction of matrices. e) Multiplication of matrices. 8. Write a Java Program to solve Tower’s of Hanoi problem. 9. Write a Java Program that uses a recursive function to compute ncr. (Note: n and r values are given.) 10. Write a Java program to perform the following operations: a) Concatenation of two strings. b) Comparison of two strings. 11. Implement the complex number ADT in Java using a class. The complex ADT is used to represent complex numbers of the form c=a+ib, where a and b are real numbers. The operations supported by this ADT are: a) Reading a complex number. d) Subtraction of complex numbers. b) Writing a complex number. e) Multiplication of complex numbers. c) Addition of Complex numbers.f) Division of complex numbers. 12. Write a Java program that makes frequency count of letters in a given text. 13. Write a Java program that uses functions to perform the following operations: a) Inserting a sub-string in to the given main string from a given position. b) Deleting n characters from a given position in a given string. 14. a) Write a Java program that checks whether a given string is a palindrome or not. Ex: MADAM is a palindrome. b) Write a Java program to make frequency count of words in a given text. 15 .a) Write a Java program that reads a file name from the user, then displays information about whether the file exists, whether the file is readable, whether the file is writable, the type of file and the length of the file in bytes. b) Write a Java program that reads a file and displays the file on the screen, with a line number before each line. c) Write a Java program that displays the number of characters, lines and words in a text file. d) Write a Java program to change a specific character in a file. Note: Filename, number of the byte in the file to be changed and the new character are specified on the command line.
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16. Write a Java program that: i) Implements stack ADT. ii) Converts infix expression into Postfix form iii) Evaluates the postfix expression. 17. a) Develop an applet in Java that displays a simple message. b) Develop an applet in Java that receives an integer in one text field, and computes its factorial Value and returns it in another text field, when the button named “Compute” is clicked. 18. Write a Java program that works as a simple calculator. Use a grid layout to arrange buttons for the digits and for the +, -,*, % operations. Add a text field to display the result. 19. Write a Java program for handling mouse events. 20. a) Write a Java program that creates three threads. First thread displays “Good Morning” every one second, the second thread displays “Hello” every two seconds and the third thread displays “Welcome” every three seconds. b) Write a Java program that correctly implements producer consumer problem using the concept of inter thread communication. 21. Write a Java program that creates a user interface to perform integer divisions. The user enters two numbers in the text fields, Num1 and Num2. The division of Num1 and Num2 is displayed in the Result field when the Divide button is clicked. If Num1 or Num2 were not an integer, the program would throw a Number Format Exception. If Num2 were Zero, the program would throw an Arithmetic Exception Display the exception in a message dialog box. 22. Write a Java program that implements a simple client/server application. The client sends data to a server. The server receives the data, uses it to produce a result, and then sends the result back to the client. The client displays the result on the console. For ex: The data sent from the client is the radius of a circle, and the result produced by the server is the area of the circle. (Use java.net) 23. a) Write a Java program that simulates a traffic light. The program lets the user select one of three lights: red, yellow, or green. When a radio button is selected, the light is turned on, and only one light can be on at a time No light is on when the program starts. b) Write a Java program that allows the user to draw lines, rectangles and ovals. 24. a) Write a Java program to create an abstract class named Shape that contains an empty method named numberOfSides ( ).Provide three classes named Trapezoid, Triangle and Hexagon such that each one of the classes extends the class Shape. Each one of the classes contains only the method numberOfSides ( ) that shows the number of sides in the given geometrical figures. b) Suppose that a table named Table.txt is stored in a text file. The first line in the file is the header, and the remaining lines correspond to rows in the table. The elements are separated by commas. Write a java program to display the table using Jtable component. 25. Write a Java program that illustrates the following a) Creation of simple package. b) Accessing a package. c) Implementing interfaces. 26. Write Java programs that illustrates the following a) Handling predefined exceptions b) Handling user defined exceptions 27. Write Java programs that use both recursive and non-recursive functions for implementing the following searching methods: a) Linear search b) Binary search 28. Write Java programs to implement the following using arrays and linked lists a) List ADT 29. Write Java program to implement the following using an array. a) Queue ADT 30. Write a Java program for handling Key events. 31. Write a Java program that uses both stack and queue to test whether the given string is a palindrome. 32. Write Java programs to implement the following using a singly linked list. a) Stack ADT b) Queue ADT 33. Write Java programs to implement the dequeue (double ended queue) ADT using a) Array b) Singly linked list c) Doubly linked list. 34. Write a Java program to implement priority queue ADT. 35. Write a Java program to perform the following operations: a) Insert an integer element into a binary search tree. b) Search for a key integer element in a binary search tree. 36. Write a Java program to implement all the functions of a dictionary (ADT) using Hashing.
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37. Write a Java program to implement circular queue ADT using an array. 38. Write Java programs that use recursive and non-recursive functions to traverse the given binary tree in a) Preorder b) Inorder and c) Postorder. 39. Write Java programs for the implementation of bfs and dfs for a given graph. 40. Write Java programs for implementing the following sorting methods: a) Bubble sort d) Quick sort b) Selection sort e) Merge sort c) Insertion sort f) Heap sort 41. Write a Java program to perform the following operation a) Insertion into a B-tree 42. Write a Java program that uses recursive functions a. To create a binary search tree. b. To count the number of leaf nodes. c. To copy the above binary search tree. 43. Write a Java program for implementing KMP pattern matching algorithm. Note: You may use packages like java.io,java.util,java.net,java.awt etc in solving the above problems. TEXT BOOKS: th
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Core Java 2, Vol I, Fundamentals, 7 Edition, C.Horstman, Gary Cornel, Pearson Education. Core Java 2, Vol 2, Fundamentals, 7th Edition, C.Horstman, Gary Cornel, Pearson Education. Introduction to Java programming, Sixth edition, Y.Daniel Liang, Pearson Education nd Data structures and algorithms in Java, 2 Edition, R.Lafore, Pearson Education. Data Structures using Java, D.S.Malik and P.S. Nair,Thomson.
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) REMOTE SENSING & GIS Objectives: To provide an introduction to remote sensing of the environment. To implement the principles of image interpretation and remote sensing in relation to optical, thermal, and radar imaging. To understand the study of spatial and environmental relationships. To understand the Analyzing operations. UNIT I Fundamentals: Definition – scope – types and chronological development – ideal and real remote sensing system. Comparison of conventional survey, aerial remote sensing and satellite remote sensing – advantage and limitation of satellite remote sensing. EMR and Remote Sensing: Energy sources – electromagnetic radiation – spectral regions – energy interaction in the atmosphere – atmosphere window – energy interaction with earth surface features – spectral reflectance patterns for different region of EMR. Actors affecting remote sensing signatures. Platforms- data capture types and systems – data recording methods.
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UNIT II INTRODUCING GIS AND SPATIAL DATA Definition – maps spatial information – computer assisted mapping and analysis – components of GIS – people and GIS – maps and spatial data – thematic characteristics of spatial data ad GPS coordinate system – other sources of spatial data; census ad survey data, air photos, satellite images, field data. DATA ANALYZING OPERATION IN GIS Terminology’s measurements of lengths, perimeter and area in GIS – queries – reclassification buffering and neighborhood functions – integrated data.
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UNIT III Raster and vector overlay method: point-in-polygon and polygon and polygon-on- polygon problems of raster and vector overlays – spatial interpolation – GIS for surface analysis – network analysis: shortest path problem, location – allocation of resources – route tracking. Models of spatial processes: natural and scale analogue models – conceptual models – mathematical model – models of physical and environmental processes. Maps as output – alternative cartographic outputs – non – cartographic outputs – spatial multimedia – delivery mechanism – GIS and spatial decision supports – maps as decision tools. UNIT IV: REMOTE SENSING APPLICATIONS Remote sensing data: types – digital, analogue – fluvial land forms - - drainage pattern – erosional and depositional landforms – flood plain mapping – coastal landforms -- erosional and depositional features – glacial landforms. UNIT V Land use/land cover: Corp assessment, disease detection, forestry: types – species identification and diseases detection. Soils: soil mapping – soil moisture – soil erosion – reservoir station – soil salinity – soil conservation. Water resources: surface water resources – water quality monitoring and mapping – water pollution, identification of ground water potential recharge areas – integrated watershed development. TEXT BOOK: 1. Remote sensing and Image interpretation – Lilesand, TM John, Wiley. REFERENCE BOOK: 1. An Introduction to Geographical Information Systems, Ian Heywood, Sarah Cornelius, Steve Carver, Srinivasa Raju, Pearson Education, 2007
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) NEURAL NETWORKS Objectives: To survey of attractive applications of artificial neural networks. To practical approach for using artificial neural networks in various technical, organizational and economic applications. UNIT I INTRODUCTION - What is a neural network? Human Brain, Models of a Neuron, Neural networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks (p. no’s 1 –49) LEARNING PROCESS – Error Correction learning, Memory based learning, Hebbian learning
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UNIT II Competitive, Boltzmann learning, Credit Assignment Problem, Memory, Adaption, Statistical nature of the learning process, (p. no’s 50 –116) SINGLE LAYER PERCEPTRONS – Adaptive filtering problem, Unconstrained Organization Techniques, Linear least square filters, least mean square algorithm, learning curves, Learning rate annealing techniques, perceptron –convergence theorem, Relation between perceptron and Bayes classifier for a Gaussian Environment (p. no’s 117 –155)
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UNIT III MULTILAYER PERCEPTRON – Back propagation algorithm XOR problem, Heuristics, Output representation and decision rule, Computer experiment, feature detection, (p. no’s 156 –201) BACK PROPAGATION - back propagation and differentiation, Hessian matrix, Generalization, Cross validation, Network pruning Techniques, Virtues and limitations of back propagation learning, Accelerated convergence, supervised learning. (p. no’s 202 –234)
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UNIT IV SELF ORGANIZATION MAPS – Two basic feature mapping models, Self organization map, SOM algorithm, properties of feature map, computer simulations, learning vector quantization, Adaptive patter classification, Hierarchal Vector quantilizer, contexmel Maps (p. no’s 443 –469, 9.1 –9.8 )
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UNIT V NEURO DYNAMICS – Dynamical systems, stability of equilibrium states, attractors, neurodynamical models, manipulation of attractors as a recurrent network paradigm (p. no’s 664 –680, 14.1 –14.6) HOPFIELD MODELS – Hopfield models, computer experiment I (p. no’s 680-701, 14.7 –14.8) TEXT BOOK: nd 1. Neural networks A comprehensive foundations, Simon Haykin, Pearson Education 2 2004 REFERENCE BOOKS: 1. Artificial neural networks - B.Yegnanarayana, Prentice Hall, 2005 2. Neural networks in Computer intelligence, Li Min Fu, TMH 2003 3. Neural networks, James A Freeman David M S Kapura, Pearson Education 2004
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) ROBOTICS AND MACHINE VISION Objectives: To Understand RPY representations To design the concepts of jacobian concepts To develop the detection techniques To compare the optimal edge detectors UNIT I Introduction evaluation of robots and robotics, classification of robots, robot anatomy, characteristics of human arm, design and control issues; manipulation and control; sessions used in robot, robot programming languages-characteristics of task level and robot level languages. Robotation matrices, euler angles and RPY representation, homogeneous transformation matrices, Demerit Huntenburg notation, direct and inverse kinematics for common types of robots for position and orientation.
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UNIT II Manipulator differential motion and statics: linear and angular velocity of a rigid body, transformation matrix and angular velocity: velocity propagation along links: manipular jacobian, jacobian inverse, jacobian singularities, Lagranzian mechanics. Lagranzian-Eular formulation of control of joints through computed torques. Control of manipulation: open and closed loop control, manipulation control problem, force control strategies.
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UNIT III Advanced edge-Detection techniques: the canny and the shen-castan methods The purpose edge detection, traditional approaches and theory, edge models: Marr-Hilderth edge detection, the shen-castan(ISEF)edge detector, A comparison of two optimal edge detectors, Source code for the Marr-Hilderth edge detector, Source code for the canny edge detector, Source code for shen –castan edge detector.
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UNIT IV Advanced methods in Grey level segmentation Basics of grey-level segmentation, the use of regional thresholds, relaxation methods, Moving averages Skeletonization-The essential line - What is a skeleton?, the medial-Axis transform, Iterative morphological methods, Use of contours, Use of objects outlines-line following, treating object as a polygon, Force based thinning, Source code for Zhang-suen/ stentifford / holt combined algorithm. UNIT V Wavelets - Essentials of wavelet decomposition, objects and 2D wavelets, Optical character recognition - The problem, OCR simple perfect images, OCR on scanned images-segmentation, OCR on fax images printed characters. Symbol recognition - Hand printed characters, the use of multiple classifiers, Printed music recognition-a study, Source code for neural net recognition system. Industrial Application - Mechanical, hydraulic and pneumatic grippers do and as servomotors, position measuring transducers, optical encoders. Industrial Application: robots in material handling: loading and unloading robots used in painting: robots in hazardous areas: specification of degrees of freedom in various applications. TEXT BOOKS: 1. Robotics-control, sensing, Vision and intelligence – Fu.K.S. Gonzalez, R.C – Mc.GH edition. 2. Industrial Robotics – Groover, MP - MGH REFERENCE BOOKS: 1. Foundations of Robotics : Analysis & Control – Robert J.Schilling, Pearson Education 2. Foundations of Robotics – Analysis and control – I/O shikawa. T – PHI. 3. Computer Evidence:Collection & Preservation, Christopher L.T.Brown, Firewall Media 4. Network Security: The complete reference, Robert Bragg, Mark Rhodes, TMH
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) PARALLEL ALGORITHMS Objectives: To learn parallel and distributed algorithms development techniques for shared memory and message passing models. To study the main classes of parallel algorithms. To study the complexity and correctness models for parallel algorithms. UNIT-I Sequential model, need of alternative model, parallel computational models such as PRAM, LMCC, Hypercube, Cube Connected Cycle, Butterfly, Perfect Shuffle Computers, Tree model, Pyramid model, Fully Connected model, PRAM-CREW, EREW models, simulation of one model from another one. UNIT-II Performance Measures of Parallel Algorithms, speed-up and efficiency of PA, Costoptimality, Example to illustrate Cost-optimal algorithmssuch as summation, Min/Max on various models.
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UNIT-III Parallel Sorting Networks, Parallel Merging Algorithms Parallel Sorting Networks on CREW/EREW/MCC/, linear array
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UNIT-IV Parallel Searching Algorithm, Kth element, Kth element in X+Y on PRAM, Parallel Matrix Transportation and Multiplication Algorithm on PRAM, MCC, Vector-Matrix Multiplication, Solution of Linear Equation, Root finding.
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UNIT-V Graph Algorithms - Connected Graphs, search and traversal, Combinatorial Algorithms- Permutation, Combinations, Derrangements. TEXT BOOK:
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1. M.J. Quinn, “Designing Efficient Algorithms for Parallel Computer” by Mc Graw Hill. REFERENCE BOOKS:
1. Algorithms, K.A.Berman and J.L.Paul, Cengage Learning. 2. Distributed Algorithms, N.A.Lynch, Morgan Kaufmann Publishers, Elsevier. 3. Parallel Algorithms, Henri Casanova, A.Legrand, Y.Robert, Chapman &Hall/CRC, Taylor and Francis Group. 4. Handbook of Parallel Computing, S.Rajasekaran, John Reif, Chapman & Hall/CRC,Taylor and Francis Group.
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) BIOMETRICS (ELECTIVE- III) Objectives To learn the actual biometric technologies and their application in the IT and in the security systems. To learn methods for evaluation of the reliability and quality of the biometric systems. To understand the concepts of voice scan features UNIT I Introduction – Benefits of biometric security – Verification and identification – Basic working of biometric matching – Accuracy – False match rate – False non-match rate – Failure to enroll rate – Derived metrics – Layered biometric solutions.
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UNIT II Finger scan – Features – Components – Operation (Steps) – Competing finger Scan technologies – Strength and weakness. Types of algorithms used for interpretation. Facial Scan - Features – Components – Operation (Steps) – Competing facial Scan technologies – Strength and weakness.
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UNIT III Iris Scan - Features – Components – Operation (Steps) – Competing iris Scan technologies – Strength and weakness.
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UNIT IV Voice Scan - Features – Components – Operation (Steps) – Competing voice Scan (facial) technologies – Strength and weakness. Other physiological biometrics – Hand scan – Retina scan – AFIS (Automatic Finger Print Identification Systems) – Behavioral Biometrics – Signature scan- keystroke scan.
TEXT BOOKS:
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UNIT V Biometrics Application – Biometric Solution Matrix – Bio privacy – Comparison of privacy factor in different biometrics technologies – Designing privacy sympathetic biometric systems. Biometric standards – (BioAPI, BAPI) – Biometric middleware Biometrics for Network Security. Statistical measures of Biometrics. Biometric Transactions.
1. Biometrics – Identity Verification in a Networked World – Samir Nanavati, Michael Thieme, Raj Nanavati, WILEY 2. Biometrics for Network Security- Paul Reid, 1/e, Pearson Education. REFERENCE BOOK: 1. Biometrics- The Ultimate Reference- John D. Woodward, Wiley
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) DATA MINING (ELECTIVE- III) Objectives: To introduce students to the basic concepts and techniques of Data Mining. To develop skills of using recent data mining software for solving practical problems. To gain experience of doing independent study and research. To understand propagation and classification of data mining. UNIT I Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or a Data Warehouse System, Major issues in Data Mining. Data Preprocessing: Need for Preprocessing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.
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UNIT II Mining Frequent Patterns, Associations and Correlations: Basic Concepts, Efficient and Scalable Frequent Itemset Mining Methods, Mining various kinds of Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining –
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UNIT III Classification and Prediction: Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Rule-Based Classification, Classification by Backpropagation, Support Vector Machines, Associative Classification, Lazy Learners, Other Classification Methods, Prediction, Accuracy and Error measures, Evaluating the accuracy of a Classifier or a Predictor, Ensemble Methods
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UNIT IV Cluster Analysis Introduction :Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data, Constraint-Based Cluster Analysis, Outlier Analysis - Mining Streams, Time Series and Sequence Data: Mining Data Streams, Mining Time-Series Data, Mining Sequence Patterns in Transactional Databases, Mining Sequence Patterns in Biological Data, Graph Mining, Social Network Analysis and Multi relational Data Mining: UNIT V Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web. - Applications and Trends in Data Mining: Data Mining Applications, Data Mining System Products and Research Prototypes, Additional Themes on Data Mining and Social Impacts of Data Mining. TEXT BOOKS: 1. Data Mining – Concepts and Techniques - Jiawei Han & Micheline Kamber, Morgan Kaufmann nd Publishers, 2 Edition, 2006. 2. Introduction to Data Mining – Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Pearson education. 3. Data Mining Techniques – Arun K Pujari, University Press REFERENCE BOOKS: 1. Data Warehousing in the Real World – Sam Aanhory & Dennis Murray Pearson Edn Asia. 2. Data Warehousing Fundamentals – Paulraj Ponnaiah Wiley student Edition 3. The Data Warehouse Life cycle Tool kit – Ralph Kimball Wiley student edition 4. Building the Data Warehouse By William H Inmon, John Wiley & Sons Inc, 2005. 5. Data Mining Introductory and advanced topics –Margaret H Dunham, Pearson education
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) RADAR IMAGING (ELECTIVE- III) Objectives: To understanding of the physical theory underlying radar systems and the generation of radar images To understanding of the principles for processing and interpreting radar images To appreciation of the utility and limitations of radar image data
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UNIT I Introduction and nature of Radar, Radar Waveforms and Frequencies; Radar Equation, minimum detectable signal, Radar Cross Section of Targets (for Sphere and Conesphere): Transmitter power, system losses CW and Frequency modulated radar, Doppler Effect Isolation between receiver and transmitter, receiver Bandwidth requirements, FM-CW Radar, Range and Doppler measurement, measurements errors MTI and Pulse Doppler Radar, MTI-Radar with power Amplifier transmitter, delay line cancellers, Doppler filters, Tracking Radar, sequential lobing, conical scan, monopulse tracing Radar, amplitude and phase comparison monopulse Radar receivers, noise figures and noise temperature, Display types Duplexers types (branch, balanced), Phased Array antennas concepts, radiation pattern, Applications, advantages and limitations.
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UNIT II Basic concepts of radar imaging, Optical definitions Holographic concepts, the principles of computerized tomography, the principles of microwave imaging. Methods of radar imaging: Target models, Basic principles of aperture synthesis. Methods of signal processing in imaging radar, SAR signal processing and holographic radar for Earth surveys, ISAR signal processing. Coherent radar holographic and topographic processing, Quasi-holographic and holographic radar imaging of point targets, on the earth surface. Side – looking SAR as a quasi-holographic radar: The principles of hologram recording, Image reconstruction from a microwave hologram, Effects of carrier track instabilities and object’s motion on image quality. Front-looking holographic radar: hologram recording, Image reconstruction and sealing relations, the focal depth. A Tomographic approach to spotlight SAR 64: Tomographic registration of earth area Projection Tomographic algorithms for image reconstruction UNIT III Imaging radars and partially coherent targets: Imaging of extended targets, Mapping of rough sea surface, A mathematical model of imaging of partially coherent extended targets, Statistical characteristics of partially coherent target images, Statistical image characteristics for zero incoherent signal integration, Statistical image characteristics for incoherent signal integration, Viewing of low contrast partially coherent targets. UNIT IV Radar systems for rotating target imaging (a holographic approach): Inverse synthesis of ID microwave Fourier holograms, Complex ID microwave Fourier holograms and their simulation, Radar systems for rotating targets imaging (a tomographic approach): Processing in frequency and space domains, Processing in 3D viewing geometry:2D and 3D imaging, the conditions for hologram recording, Preprocessing of radar data, Hologram processing by coherent summation of partial components, Processing algorithms for holograms of complex geometry, 2D and 3D viewing Geometry. UNIT V Radar imaging applications: The earth remote sensing, Satellite SARs, SAR sea ice monitoring in the Arctic SR imaging of mesoscale ocean phenomena.
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The application of inverse aperture synthesis for radar imaging, Measurement of target characteristics, Target recognition. TEXT BOOKS: 1. Merill I Skolnik, “Introduction to Radar Systems”, 3/e, Tata Macgraw Hill, 2001 2. Alexander Ja Pasmurov and Julius S.Zinoview, “Radar, Sonar, Navigation & Avionics”, ISBN : 086341-502-4
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REFERENCE BOOKS: 1. Peyton Z.Peebles, “Radar Principles”, John Wiley, 1998, ISBN 0-4712-52050 2. Joseph C.Toland, “Radar Imaging”(Bridgeway Press)
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) GENETIC ALGORITHMS (ELECTIVE- IV) Objectives: To understand the search methods in the genetic algorithms To implement the reproduction concepts. To design the techniques of dominance in genetic algorithms. UNIT I A GENTLE INTRODUCTION TO GENETIC ALGORITHMS – Introduction to genetic algorithm, Robustness of Traditional Optimization and Search Methods, The goals of Optimization, How are Genetic Algorithms Different from Traditional Methods?, A simple genetic algorithm, Genetic algorithms at work – a simulation by hand, Grist for the Search Mill – important similarities, Similarity templates (Schemata), Learning the Lingo
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UNIT II GENETIC ALORITHMS REVISITED: MATHEMATICAL FOUNDATIONS – Who shall live and who shall die? The fundamental theorem, Schema Processing at work: An example by hand revisited, the two-armed and K-armed bandit problem, How many schemata are processed usefully?, The building block hypothesis, another perspective: the minimal Deceptive problem, Schemata revisit: similarity templates as hyper planes.
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UNIT III COMPUTER IMPLEMENTATION OF A GENETIC ALGORITHM – Data structures, reproduction, crossover, and mutation, A time to reproduce, a time to cross, get with the main program, How well does it work? Mapping objective functions to fitness form, fitness scaling, codings, a multiparameter, mapped, fixed-point coding, discretization, constriants
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UNIT IV SOME APPLICATIONS OF GENETIC ALGORITHMS – The rise of genetic algorithms, Genetic algorithm applications of historical interest, de-jong and function optimization, improvements in basic technique, current applications of genetic algorithms - ADVANCED OPERATIONS AND TECHNIQUES IN GENETIC SEARCH – Dominance, diploidy and abeyance, inversion and other reordering operators, other micro operators, niche and speciation, multiobjective optimization Knowledge-based techniques, genetic algorithms and parallel processors UNIT V INTRODUCTION TO GENETICS-BASED MACHINE LEARNNG – Genetic-based machine learning: Whence it came, what is a classifier system? Rule and message system, apportionment of credit: the bucket brigade, genetic algorithm, a simple classifier system in Pascal, results using the simple classifier system - APPLICATIONS OF GENETICS-BASED MACHINE LEARNING – The rise of GBML, development of CS-I, the first classifier system, smith’s poker player, other early GBML efforts, a potpourri of current applications TEXT BOOK: 1. David E.Goldberg, “Genetic Algorithms” – 1/e, Pearson Education. REFERENCE BOOKS: 1. Genetic algorithms in search, optimization and Mechine learning, By David E. Gold Berg Pearson Edition 2. An Introduction to Genetic Algorithm by Melanie Mitchell 3. The Simple Genetic Algorithm Foundation & Theores by Michael P. Vosk
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) STORAGE AREA NETWORKS (ELECTIVE- IV) Objectives: To understand Storage Area Networks characteristics and components. To become familiar with the SAN vendors and their products To learn Fibre Channel protocols and how SAN components use them to communicate with each other To become familiar with Cisco MDS 9000 Multilayer Directors and Fabric Switches Thoroughly learn Cisco SAN-OS features. To understand the use of all SAN-OS commands. Practice variations of SANOS features UNIT I Introduction to Storage Technology: Review data creation and the amount of data being created and understand the value of data to a business, challenges in data storage and data management, Solutions available for data storage, Core elements of a data center infrastructure, role of each element in supporting business activities
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UNIT II Storage Systems Architecture: Hardware and software components of the host environment, Key protocols and concepts used by each component ,Physical and logical components of a connectivity environment ,Major physical components of a disk drive and their function, logical constructs of a physical disk, access characteristics, and performance Implications, Concept of RAID and its components , Different RAID levels and their suitability for different application environments: RAID 0, RAID 1, RAID 3, RAID 4, RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and contrast integrated and modular storage systems ,High-level architecture and working of an intelligent storage system
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UNIT III Introduction to Networked Storage: Evolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS, and IP-SAN, Benefits of the different networked storage options, Understand the need for long-term archiving solutions and describe how CAS fulfills the need , Understand the appropriateness of the different networked storage options for different application environments
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UNIT IV Information Availability & Monitoring & Managing Datacenter: List reasons for planned/unplanned outages and the impact of downtime, Impact of downtime, Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO and RPO, Identify single points of failure in a storage infrastructure and list solutions to mitigate these failures , Architecture of backup/recovery and the different backup/recovery topologies , replication technologies and their role in ensuring information availability and business continuity, Remote replication technologies and their role in providing disaster recovery and business continuity capabilities Identify key areas to monitor in a data center, Industry standards for data center monitoring and management, Key metrics to monitor for different components in a storage infrastructure, Key management tasks in a data center UNIT V Securing Storage and Storage Virtualization: Information security, Critical security attributes for information systems, Storage security domains, List and analyzes the common threats in each domain, Virtualization technologies, block-level and file-level virtualization technologies and processes Case Studies The technologies described in the course are reinforced with EMC examples of actual solutions. Realistic case studies enable the participant to design the most appropriate solution for given sets of criteria. TEXT BOOK: 1. EMC Corporation, Information Storage and Management, Wiley.
M. TECH. IMAGE PROCESSING-R13 Regulations
REFERENCE BOOKS:
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1. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill, Osborne, 2003. 2. Marc Farley, “Building Storage Networks”, Tata McGraw Hill, Osborne, 2001. 3. Meeta Gupta, Storage Area Network Fundamentals, Pearson Education Limited, 2002.
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) MULTIMEDIA DATABASES (ELECTIVE-IV) Objectives: To understand the content-based similarity search in multimedia retrieval To understand the content-based similarity measure To understand the query types in multimedia retrieval To understand video search and its challenges UNIT I Introduction: An introduction to Object-oriented Databases; Multidimensional Data Structures: k-d Trees, Point Quad trees, The MX-Quad tree, R-Trees, comparison of Different Data Structures UNIT II Image Databases : Raw Images, Compressed Image Representations, Image Processing: Segmentation, Similarity-Based Retrieval, Alternative Image DB Paradigms, Representing Image DBs with Relations, Representing Image DBs with R-Trees, Retrieving Images By Spatial Layout, Implementations
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UNIT III Text/Document Databases: Precision and Recall, Stop Lists, Word Stems, and Frequency Tables, Latent Semantic Indexing, TV-Trees, Other Retrieval Techniques Video Databases: Organizing Content of a Single Video, Querying Content of Video Libraries, Video Segmentation, video Standards Audio Databases: A General Model of Audio Data, Capturing Audio Content through Discrete Transformation, Indexing Audio Data
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UNIT IV Multimedia Databases : Design and Architecture of a Multimedia Database, Organizing Multimedia Data Based on The Principle of Uniformity, Media Abstractions, Query Languages for Retrieving Multimedia Data, Indexing SMDSs with Enhanced Inverted Indices, Query Relaxation/Expansion Creating Distributed Multimedia Presentations: Objects in Multimedia Presentations, Specifying Multimedia Documents with Temporal Constraints, Efficient Solution of Temporal Presentation Constraints, Spatial Constraints.
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UNIT V Spatial Concepts and Data Models: Models of spatial information, Design extending the ER model with spatial concepts, Extending the ER model pictograms, Object oriented data model with UML. Spatial Query Languages: Extending the SQL for spatial data, Examples of queries that emphasis spatial data, Object relational schema examples queries. TEXT BOOKS: 1. Principles of Multimedia Database Systems, V.S. Subrahmanian, Elseveir(Morgan Kauffman). 2. Spatial Databases, Shashi Shekhar, Sanjiv Chawla, Pearson Education., 2009 REFERENCE BOOKS: 1. Multimedia Databases: An object relational approach, Lynne Dunckley, Pearson Education. 2. Multimedia Database Management Systems, B.Prabhakaran, Springer.
M. TECH. IMAGE PROCESSING-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M. Tech – I Year – II Sem. (Image Processing) IMAGE PROCESSING LAB USING MATLAB Objectives: The aim of the course is to provide basic and working knowledge for participants to perform various image processing techniques using the Image Processing Toolbox and Video and Image Processing. Part –I Basic Experiments
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1. Perform algebraic operations such as addition, subtraction, multiplication, and division. These operations can be used to perform operations on images such as noise reduction by using averages, movement detection, and algebraic masking. 2. Geometric transformations such as translation, rotation, and scaling 3. Space domain operations such as histogram modification (scaling, offset, amplitude change) non linear point operations (absolute value, squaring, square root, log scale compression, edge detection) 4. Binary image processing (thresholding, logic operations). 5. Non linear image processing such as morphologic operators (opening, closing). Structuring element choice. Dilation, erosion. 6. Frequency domain processing. Fourier transform, log compression. 7. Filtering by linear convolution. Filter design (low pass, high pass, band pass, band reject.). Gaussian Filters. Linear restoration. White noise non linear filtering. 8. Digital Image coding and compression. Compression measures, losses compression, entropy, optimal coding.
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Part – II Application based Experiments 9. Point-to-point transformation. This laboratory experiment provides for thresholding an image and the evaluation of its histogram. The user can choose a threshold level to see the image showing only the pixels at that threshold. 10. Morphological operations I. This experiment is intended so students can appreciate the effect of morphological operations using a small structuring element on simple binary images. The operations that can be performed are erosion, dilation, opening, closing, open-close, close-open. 11. Morphological operations II. This experiment is designed to let students know how morphological functions change images by applying consecutive erosion and dilation operations. 12. Histogram equalization. This experiment illustrates the relationship among the intensities (gray levels) of an image and its histogram. It shows how to improve the image by equalizing the histogram. 13. Geometric transformations. This experiment shows image rotation, scaling, and translation. 14. Two-dimensional Fourier transform I. The purpose of this experiment is to provide an understanding of the harmonic content of an image using the discrete Fourier transform (DFT). 15. Two-dimensional Fourier Transform II. This experiment is designed so the student learns the concept of masking with the DFT. 16. Linear filtering using convolution. After completing this experiment every student should understand the concepts of filtering using linear convolution. 17. Highly selective filters. In this experiment students appreciate the effects on an image after a highly selective filter is applied to it.
