DOC/LP/01/28.02.02
/ LESSON PLAN / LP- EC1009LP Rev. No: 01
Date:28/6/2010
Page 01 of 06
SubCode& Name : EC1009 DIGITAL IMAGE PROCESSING Unit: I Branch: ECE Semester:VII
Unit syllabus:
UNIT I DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS 9
Elements of visual perception – Image sampling and quantization Basic relationship between pixels – Basic geometric transformations-Introduction to Fourier Transform and DFT – Properties of 2D Fourier Transform – FFT – Separable Image Transforms -Walsh – Hadamard – Discrete Cosine Transform, Haar, Slant – Karhunen – Loeve transforms.
Objective: To study the image fundamentals and mathematical transforms necessary for image processing.
SessionNo / Topics to be covered / Time / Ref with page no / Teaching Method
Elements of visual perception,Structure of human eye / 50m / 1(34-38) / BB
Image sampling and quantization-Basic concepts, representing digital images, spatial and gray level resolution. / 50m / 1(52-64) / BB
Basic relationship between pixels – neighbors of apixel, Adjacency, connectivity, Distance measures. / 50m / 1(66-69) / BB
Basic geometric transformations ,Introduction to Fourier Transform and DFT / 50m / 4(141-146) / BB
Properties of 2D Fourier Transform and FFT / 50m / 4(147-150 / BB
Separable Image Transforms –Walsh and Hadamard Transform / 50m / 4(155-159 / BB
Discrete Cosine Transform, Haar Transform / 50m / 4(150-154,159-161) / BB
Slant Transform / 50m / 4(161-163) / BB
Karhunen – Loeve transforms / 50m / 4(163-168) / BB
/ LESSON PLAN / LP- EC1009
LP Rev. No: 01
Date: 28/6/2010
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SubCode& Name : EC1009 DIGITAL IMAGE PROCESSING Unit: II Branch: ECE Semester:VII
Unit syllabus:
UNIT II IMAGE ENHANCEMENT TECHNIQUES: 9
Spatial Domain methods: Basic grey level transformation – Histogram equalization – Image subtraction – Image averaging –Spatial filtering: Smoothing, sharpening filters – Laplacian filters – Frequency domain filters: Smoothing – Sharpening filters – Homomorphic filtering.
Objective: To study the image enhancement techniques
SessionNo / Topics to be covered / Time / Ref with page no / Teaching Method
Spatial Domain methods:
Basic grey level transformation-Image negatives, logtransformation, Power law transformation, piecewise linear transformation. / 50m / 1(78-85) / BB
Histogram equalization / 50m / 1(91-93) / BB
Image subtraction and Image averaging / 50m / 1(110-116) / BB
Smoothing, sharpening filters-Smoothing linear filters, Order statistics filter, Use of second and first derivative for enhancement. / 50m / 1(116-127) / BB
Laplacian filters / 50m / 1(128-137) / BB
Frequency domain filters : Smoothing filter-ideal,Butterworth and Gaussian low pass filter / 50m / 1(167-179) / BB
Sharpening filters-Butterworth, Gaussian, Laplacian in freq domain, Unsharpmasking, High boost filtering. / 50m / 1(180-190) / BB
Homomorphism filtering. / 50m / 1(191-194) / BB
CAT-I / 50m
/ LESSON PLAN / LP- EC1009
LP Rev. No: 01
Date: 28/6/10
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SubCode& Name : EC1009 DIGITAL IMAGE PROCESSING Unit:III Branch: ECE Semester:VII
Unit syllabus:
UNIT III IMAGE RESTORATION:9
Model of Image Degradation/restoration process – Noise models – Inverse filtering -Least mean square filtering – Constrained least mean square filtering – Blind image restoration – Pseudo inverse – Singular value decomposition.
Objective: To study image restoration procedures.
SessionNo / Topics to be covered / Time / Ref with page no / Teaching Method
Model of Image Degradation/restoration process / 50m / 1(221) / BB
Noise models-some important noise probability density functions. / 50m / 1(222-229) / BB
Inverse filtering / 50m / 1(261) / BB
Least mean square filtering / 50m / 1(262-265) / BB
Constrained least mean square filtering / 50m / 1(266-269) / BB
Blind image restoration / 50m / 2(392-396) / BB
Pseudo inverse filter / 50m / 2(365-375) / BB
Singular value decomposition. / 50m / 2(376-381) / BB
Problems / 50m / 1,2 / BB
/ LESSON PLAN / LP- EC1009
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Date: 28/6/10
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SubCode& Name : EC1009 DIGITAL IMAGE PROCESSING Unit: IV Branch: ECE Semester:VII
Unit syllabus:
UNIT IVIMAGE COMPRESSION 9
Lossless compression: Variable length coding – LZW coding – Bit plane coding- predictive coding-DPCM.
Lossy Compression: Transform coding – Wavelet coding – Basics of Image compression standards: JPEG, MPEG,Basics of Vector quantization.
Objective: To study the image compression procedures.
SessionNo / Topics to be covered / Time / Ref with page no / Teaching Method
30 / Lossless compression: Variable length coding / 50m / 1(440-446) / BB
31 / LZW coding / 50m / 1(446-448) / BB
32 / Bit plane coding,lossless predictive coding / 50m / 1(448-456) / BB
33 / DPCM / 50m / 1(459-467) / BB
34 / Lossy Compression: Transform coding / 50m / 1(467-485) / BB
35 / Wavelet coding / 50m / 1(486-492) / BB
36 / Basics of Image compression standards: JPEG / 50m / 1(492-510) / BB
37 / MPEG / 50m / 1(510-512) / BB
38 / Basics of Vector quantization / 50m / 2(156-159) / BB
39 / CAT-II / 75m
/ LESSON PLAN / LP- EC1009
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SubCode& Name : EC1009 DIGITAL IMAGE PROCESSING Unit: V Branch: ECE Semester:VII
Unit syllabus:
UNIT V IMAGE SEGMENTATION AND REPRESENTATION 9
Edge detection – Thresholding - Region Based segmentation – Boundary representation: chair codes- Polygonal approximation – Boundary segments – boundary descriptors: Simple descriptors-Fourier descriptors - Regional descriptors –Simple descriptors- Texture
Objective: To study the image segmentation and representation techniques.
SessionNo / Topics to be covered / Time / Ref with page no / Teaching Method
Edge detection / 50m / 1(585-594) / BB
Thresholding-Role of illumination, Global and adaptive Thresholding,Threshold based on Several variables / 50m / 1(595-611) / BB
Region Based segmentation-Region growing, Splitting and merging / 50m / 1(612-616) / BB
Boundary representation: chain codes, Polygonal approximation / 50m / 1(644-649) / BB
Boundary segments / 50m / 1(649-653) / BB
Boundary descriptors: Simple descriptors, Fourier descriptors / 50m / 1(653-660) / BB
Regional descriptors- Simple descriptors / 50m / 1(660-665) / BB
Texture / 50m / 1(665-672) / BB
CAT-III / 75m
/ LESSON PLAN / LP- EC1009
LP Rev. No: 01
Date: 28/6/10
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SubCode& Name : EC1009 DIGITAL IMAGE PROCESSING Unit: I -V Branch: ECE Semester:VII
TEXT BOOKS
- Rafael C Gonzalez, Richard E Woods 2nd Edition, Digital Image Processing - Pearson Education 2003.
REFERENCES
- William K Pratt, Digital Image Processing John Willey (2001)
- Image Processing Analysis and Machine Vision – Millman Sonka, Vaclav hlavac, Roger Boyle, Broos/colic, Thompson Learniy (1999).
- A.K. Jain, PHI, New Delhi (1995)-Fundamentals of Digital Image Processing.
- Chanda Dutta Magundar – Digital Image Processing and Applications, Prentice Hall of India, 2000
Course Delivery Plan:
Week / 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 / 9 / 10 / 11 / 12 / 13I II / I II / I II / I II / I II / I II / I II / I II / I II / I II / I II / I II / I II
Units / / 1 / / 2 / / 3 / / 4 / 5
Text Books:
Prepared by / Approved bySignature
Name / K.Srividhya/B.Sarala / Prof.E.G.GovindanDesignation / Senior Lecturer/Lecturer / HOD/EC
Date / 28/6/2010 / 28/6/2010