Lecture plan DIGITAL IMAGE PROCESSING (ECS-702) |
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Unit |
Topic |
Lecture |
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Unit I |
Introduction and Fundamentals, Motivation and Perspective, Applications, Components of Image Processing System, |
Lecture 1 |
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Element of Visual Perception, A Simple Image Model, |
Lecture 2 |
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Sampling and Quantization. |
Lecture 3 |
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Image Enhancement in Frequency Domain: Fourier Transform and the Frequency Domain, |
Lecture 4 |
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Basis of Filtering in Frequency Domain, |
Lecture 5 |
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Filters – Low-pass, High-pass; |
Lecture 6 |
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Correspondence Between Filtering in Spatial and Frequency Domain; |
Lecture 7 |
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Smoothing Frequency Domain Filters – Gaussian Lowpass Filters; |
Lecture 8 |
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Sharpening Frequency Domain Filters – Gaussian Highpass Filters; Homomorphic Filtering. |
Lecture 9 |
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Unit II |
Image Enhancement in Spatial Domain: Introduction; Basic Gray Level Functions |
Lecture 10 |
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– Piecewise-Linear Transformation Functions: Contrast Stretching; |
Lecture 11 |
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Histogram Specification; |
Lecture 12 |
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Histogram Equalization; |
Lecture 13 |
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Local Enhancement; |
Lecture 14 |
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Enhancement using Arithmetic/Logic Operations – Image Subtraction, Image Averaging; |
Lecture 15 |
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Basics of Spatial Filtering; Smoothing - Mean filter, |
Lecture 16 |
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Ordered Statistic Filter; |
Lecture 17 |
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Sharpening – The Laplacian. |
Lecture 18 |
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Unit III |
Image Restoration A Model of Restoration Process, Noise Models, |
Lecture 19 |
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Restoration in the presence of Noise only-Spatial Filtering |
Lecture 20 |
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Mean Filters: Arithmetic Mean filter, Geometric Mean Filter, Order Statistic Filters – |
Lecture 21 |
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Median Filter, Max and Min filters; Periodic Noise Reduction by Frequency Domain Filtering – |
Lecture 22 |
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Bandpass Filters; Minimum Mean-square Error Restoration. |
Lecture 23 |
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Unit IV |
M orphological Image Processing Introduction, Logic Operations involving Binary Images, Dilation and Erosion |
Lecture 24 |
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Opening and Closing, Morphological Algorithms – Boundary Extraction, Region Filling, |
Lecture 25 |
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Extraction of Connected Components, Convex Hull, Thinning, Thickening |
Lecture 26 |
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Unit V |
Introduction, Geometric Transformation – Plane to Plane transformation, |
Lecture 27 |
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Mapping, Stereo Imaging – Algorithms to Establish Correspondence, |
Lecture 28 |
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Algorithms to Recover Depth |
Lecture 29 |
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Segmentation Introduction, Region Extraction, Pixel-Based Approach, |
Lecture 30 |
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Multi-level Thresholding, Local Thresholding, Region-based Approach, |
Lecture 31 |
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Edge and Line Detection: Edge Detection, Edge Operators, |
Lecture 32 |
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Pattern Fitting Approach, Edge Linking and Edge Following, Edge Elements Extraction by Thresholding, |
Lecture 33 |
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Edge Detector Performance, Line Detection, Corner Detection. |
Lecture 34 |
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References: 1. Digital Image Processing 2nd Edition, Rafael C. Gonzalvez and Richard E. Woods. Published by: Pearson Education. 2. Digital Image Processing and Computer Vision, R.J. Schalkoff. Published by: John Wiley and Sons, NY. 3. Fundamentals of Digital Image Processing, A.K. Jain. Published by Prentice Hall, Upper Saddle River, NJ. |
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