| Sl. No. | Units | |||
| 1. | Introduction to image processing and computer vision | |||
| 2. | Image segmentation: -Thresholding, -Edge-based, -Region based, -Active Contour |
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| 3. | Feature Extraction, Description and Matching: -Geometric features (e.g., lines, circles, ellipses), -Blobs |
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| 4. | Object Recognition: -Geometry-based (e.g., alignment, geometric hashing), -Appearance-based (e.g., subspace, bag-of-features), -Applications (i.e., 2D/3D object recognition, face recognition) |
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| 5. | 3D vision, geometry: -Basics of projective geometry -Overview of single camera calibration -The geometry of two cameras, relative motion of the camera -Stereo correspondence algorithms |
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| 6. | Image/Video Tracking | |||
| Assignments |
Experiments are assigned to the students to learn methods for acquiring, processing, analyzing, and understanding digital images from the real world in order to produce numerical or symbolic information, e.g., in the form of decisions. Experiments include problems, like, various Feature Extractions: edge, corner, and blob detections; Feature Representation; Texture Classification; Feature Matching for finding correspondences in a pair of 2D images; Camera Calibrations; Object Detection and Recognitions; Face Recognitions; 3D-Reconstructions from 2D Images; and Motion Tracking, etc. All experiments are exclusively implemented using OpenCV. |
| Text Books |
| 1. D. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice-Hall, 2001. 2. E. R. Davies, Computer and Machine Vision, 4/e, Elsevier academic press, 2012. |
| Reference Books |
| 1. Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, Machine Vision, McGraw-Hill, 1995. 2. Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing, Analysis, and Machine Vision, Thompson Learning, 2008. 3. R. C. Gonzalez and R.E. Woods, Digital Image Processing, Pearson Education, 2001. 4. A.K, Jain, Fundamentals of Digital Image Processing, Pearson Education, 1989. |