Hello sir will you please give me here a PDF of Image Processing Syllabus from Pune University?

The Pune University is provides BE course in computer science. In this stream having a subject this is Image Processing. This course syllabus of Image Processing is provides detail of image processing.

Image Processing Syllabus from Pune University
Unit Content Hrs
I Problem solving and Algorithmic Analysis 6
Problem solving principles: Classi_cation of problem, problem solving
strategies, classi_cation of time complexities (linear, logarithmic etc)
problem subdivision { Divide and Conquer strategy.
Asymptotic notations, lower bound and upper bound: Best case, worst case,
average case analysis, amortized analysis. Performance analysis of basic
programming constructs. Recurrences: Formulation and solving recurrence
equations using Master Theorem.
II Greedy and Dynamic Programming Algorithmic Strategies 6
Greedy strategy: Principle, control abstraction, time analysis of control
abstraction, knapsack problem, scheduling algorithms-Job scheduling and
activity selection problem.
Dynamic Programming: Principle, control abstraction, time analysis of
control abstraction, binomial coe_cients, OBST, 0/1 knapsack, Chain Matrix
multiplication.
III Backtracking and Branch-n-Bound 8
Backtracking: Principle, control abstraction, time analysis of control
abstraction, 8-queen problem, graph coloring problem, sum of subsets
problem.
Branch-n-Bound: Principle, control abstraction, time analysis of control
abstraction, strategies { FIFO, LIFO and LC approaches, TSP, knapsack
problem.
IV Complexity Theory 6
Overview: Turing machine, polynomial and non-polynomial problems,
deterministic and non-deterministic algorithms, P class, NP class &
NP complete problems- vertex cover and 3-SAT and NP{hard problem {
Hamiltonian cycle. The menagerie of complexity classes of Turing degrees.
Concept of randomized and approximation algorithms: Solving TSP by
approximation algorithm, Randomized sort algorithms and Approximating
Max Clique.
V Parallel and Concurrent Algorithms 6
Parallel Algorithms: Sequential and parallel computing, RAM &
PRAM models, Amdahl's Law, Brent's theorem, parallel algorithm analysis
and optimal parallel algorithms, graph problems (shortest paths and
Minimum Spanning Tree, Bipartite graphs )
Concurrent Algorithms: Dining philosophers problem
VI Algorithmic Case-studies 8
Distributed Algorithms: Bully algorithm { method for dynamically
selecting a coordinator, all pair shortest path (Floyed-Warshall Algorithm),
Dijkstra-Scholten algorithm { detection of process termination, Buddy
memory algorithm { method to allocate memory.
Embedded Algorithms: Embedded system scheduling (power optimized
scheduling algorithm), sorting algorithm for embedded systems.
Internet of Things and Data Science Algorithms: Algorithms in IoT:
Cryptography Algorithms, Scheduling Algorithms, Data management Algorithms
and clustering, context management. Data Science Project Life Cycle(DSPLC),
Mathematical Considerations: Mathematical modeling, Optimization Methods, Adaptive
and Dynamic Algorithms and Numerical Analysis in IoT
Algorithms in Software Engineering: String matching algorithm-
Boyer-Moore algorithm KMP algorithm.

Unit Content Hrs
I Notion and Concepts 6
Introduction to compilers { Design issues, passes, phases, symbol table
Preliminaries { Memory management, Operating system support for compiler,
Compiler support for garbage collection
Lexical Analysis { Tokens, Regular Expressions, Process of Lexical analysis, Block
Schematic, Automatic construction of lexical analyzer using LEX, LEX features and
speci_cation
II Parsing 8
Syntax Analysis { CFG, top-down and bottom-up parsers, RDP, Predictive parser, SLR,
LR(1), LALR parsers, using ambiguous grammar, Error detection and recovery,
automatic construction of parsers using YACC, Introduction to Semantic analysis {
Need of semantic analysis, type checking and type conversion
III Syntax Translation Schemes 7
Syntax Directed Translation and Intermediate Code Generation { Attribute grammar, S
and L attributed grammar, bottom up and top down evaluations of S and L attributed
grammar, Intermediate code { need, types, Syntax directed translation scheme,
Intermediate code generation for - assignment statement, declaration statement,
Boolean expression, if-else statement, do -while statement, array assignment.
IV Code Generation and Optimization 8
Code Generation and Code Optimization { Issues in code generation, basic blocks,
ow graphs, DAG representation of basic blocks, Target machine description,
Register allocation and Assignment, Simple code generator, Code generation from
labeled tree, Concept of code generator.
Need for Optimization, local, global and loop optimization, Optimizing transformations
{ compile time evaluation, common sub-expression elimination, variable propagation,
code movement, strength reduction, dead code elimination, DAG based local optimization,
peephole optimization, Introduction to global data ow analysis, Data ow equations
and iterative data ow analysis (only introduction expected)
V Functional and Logic Programs 7
Language Speci_c Compilation: Object Oriented languages { source language issues,
routines and activation, code generation and control ow
Functional languages - introduction to Functional Programs, basic compilation,
polymorphic type checking, desugaring , compiling to a register-oriented architectures
JavaCC (Chapter 13 of reference book 1)
VI Parallel and Distributed Compilers 8
Parallel programming models, Processes and threads, Shared variables
Message passing, Parallel Object Oriented languages, Tuple space, Automatic
parallelization
Introduction to advanced topics { JIT, Dynamic compilation, Interpreters (JVM/Dalvik),
Cross compilation using XMLVM, Case studies(self study): GCC, g++,
nmake,cmake. NVCC (case study for parallel compilation), LLVM
Unit Content Hrs
I Introduction to Intelligent Systems 4
Introduction, History, Foundations and Mathematical treatments, Problem solving
with AI, AI models, Learning aspects in AI, What is an intelligent Agents,
Rational agent, Environments types, types of Agents
II Problem-solving and Building Smart Systems 6
Problem solving process, Problem analysis and representation, Problem space and search,
Toy problems, real world problems, Problem reduction methods,
General Search algorithms, Uninformed Search methods, Informed (Heuristic) Search
Best-_rst, Greedy, A* search methods, Heuristic Functions, AO*, Local Search
Algorithms and optimization problems, Adversarial search methods,
Important concepts of Game theory, Game theory and knowledge structure, Game as a
search problem, Alpha-Beta Pruning, Stochastic Games,
Constraint Satisfaction Problem, CSP as search problem
III Knowledge, Reasoning, and Planning 7
Knowledge based agents, The Wumpus World, Logic, propositional logic, Representation
of knowledge using rules, Predicate logic, Uni_cation and lifting, inference in FOL,
Forward Chaining, Backward Chaining, Resolution, Logic Programming.
Planning problem, Planning, Algorithms for Planning as State-Space Search, Planning
Graphs, simple planning agent, planning languages, blocks world problem, goal stack
planning, mean end analysis, progression planners, regression planners, partial
order planning, planning graphs, hierarchical planning, job shop scheduling problem,
Planning and Acting in the Real World, Hierarchical Planning, Multi-agent Planning,
Ontological Engineering, Categories and Objects, Events, Mental Events and Mental
Objects, Reasoning Systems for Categories, Reasoning with Default Information,
The Internet Shopping World
IV Uncertain Knowledge and Decision Theory 6
Uncertainty and methods, Basic Probability Notion, Inference Using Full Joint
Distributions, Bayesian probability and belief networks, Relational and First-
Order Probability Models, Other techniques in uncertainty and reasoning, Inference
in Temporal Models, Hidden Markov Models, Kalman Filters, Dynamic Bayesian
Networks, Decision network, Semi-constraint inuence diagram, Decision making and
imperfect information, Combining Beliefs and Desires under Uncertainty, The Basis of
Utility Theory, Utility Functions, Multi-attribute Utility Functions, Decision
Networks, Decision-Theoretic Expert Systems
V Learning Tools, Techniques and Applications 7
Machine Learning Concepts, methods and models, Supervised Learning, unsupervised
and semi-supervised, Learning Decision Trees, Evaluating and Choosing
the Best Hypothesis, Arti_cial Neural Networks, Non-parametric Models, Support
Vector Machines, Ensemble Learning, empirical learning tasks, Explanation-Based
Learning, Inductive Logic Programming, Reinforcement Learning, Active Learning,
Learning based on limited information.
Building Smart systems using di_erent learning techniques, smart system applications,
agent based concurrent engineering
VI Communicating, Perceiving, and Acting 6
Language Models, Text Classi_cation, Information Retrieval, Information Extraction,
Phrase Structure Grammars, Syntactic Analysis (Parsing), Augmented Grammars and
Semantic Interpretation, Machine Translation, Speech Recognition, Image Formation
and object recognition, Early Image-Processing Operations, Object Recognition by
Appearance, Reconstructing the 3D World, Object Recognition from Structural
Information, Using Vision, Robot Hardware, Robotic Perception, Planning to Move,
Planning Uncertain Movements, Robotic Software Architectures, Application Domains

Syllabus of Image Processing:

Unit I
What is digital image processing? Origin, usage and application of image processing. Fundamental steps and component of image processing system. Introduction to Human Visual System. Digital representation of images (monochrome & color). Elements of matrix theory, Digital Imaging Hardware & Software.

Unit II
Basic image preprocessing (contrast enhancement, simple noise reduction, color balancing), Spatial transformation Gray Level liner and non-linear transformation, Histogram Processing, Hadamard and Walsh transformation. Image enhancement in spatial and frequency domain: basic fundamental, smoothing and sharpening domain filters. Sampling & Quantization.

Unit III
Image Processing filters, Image Segmentation & Analysis, Implementation Feature extraction: Edges, Lines & corners detection, Texture & shape measures.
Segmentation & thresholding, region extraction, edge (Canny) & region based approach, use of motion in segmentation. Feature extractionEdges, Lines & corners detection, Texture & shape measures.

Unit IV
Image Restoration & Reconstruction. Introduction, Model of Image degradation, Noise Models, Classification of image restoration techniques, Blind-deconvolution techniques, Lucy Richardson Filtering, Wiener Filtering.

Unit V
Image Compression & Object Recognition. Introduction to Image Compression and its need, Coding Redundancy, Classification of Compression Techniques (Lossy and Losless - JPEG, RLE, Huffman, Shannon fano), Scalar & Vector Quantization. Introduction to Object Recognition, Object Representation (Signatures, Boundary Skeleton), Simple Boundary Descriptors, Regional descriptors(Texture).

Unit VI:
Wavelets & Application of Image Processing. Background: Image pyramids, Sub-band coding, Haar and Daubechies Wavelets. Image Compression using Wavelets (JPEG 2000). Principal Component Analysis & Local Component Analysis for dimension reduction. Applications Medical Image Processing, Face detection, Iris Recognition

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