IT379 COMPUTER VISION

Course Coordinator: - Prof. Sanket Suthar




Title of the unit Minimum number of hours
1
Introduction and Foundations
05
2 Digital Image Formation and low-level processing 04
3
Depth estimation and multi-camera views 		03
4 Feature Extraction, Image Segmentation and Pattern Analysis 10
5 Shape Representation and Segmentation 07
6 Hough Transform and Object recognition 07
7 3D Vision and Motion 05
8 Applications 04


Unit No. Topics Teaching Hours
1
Introduction and Foundations Image Processing, Computer Vision and Computer Graphics, Overview of Diverse Computer Vision Applications: Document Image Analysis, Biometrics, Object Recognition, Tracking, Medical Image Analysis, Content-Based Image Retrieval, Video Data Processing, Multimedia, Virtual Reality and Augmented Reality
05
2 Digital Image Formation and low-level processing Overview and State-of-the-art, Fundamentals of Image Formation, Transformation: Orthogonal, Euclidean, Affine, Projective, etc; Fourier Transform, Convolution and Filtering, Image Enhancement, Restoration, Histogram Processing. 04
3
Depth estimation and multi-camera views Perspective, Binocular Stereopsis: Camera and Epipolar Geometry; Homography, Rectification, DLT, RANSAC, 3-D reconstruction framework; Auto-calibration. Apparel. 		03
4 Feature Extraction, Image Segmentation and Pattern Analysis Edges - Canny, LOG, DOG; Line detectors (Hough Transform), Corners - Harris and Hessian Affine, Orientation Histogram, SIFT, SURF, HOG, GLOH, Scale-Space Analysis- Image Pyramids and Gaussian derivative filters, Gabor Filters and DWT, Segmentation: Region Growing, Edge Based approaches to segmentation, Graph-Cut, Mean-Shift, MRFs, Texture Segmentation; Object detection, Pattern Analysis: Clustering: KMeans, K-Medoids, Mixture of Gaussians, Classification: Discriminant Function, Supervised, Un-supervised, Semi-supervised; Classifiers: Bayes, KNN, ANN models; Dimensionality Reduction: PCA, LDA, ICA; Non-parametric methods. 10
5 Shape Representation and Segmentation Contour based representation, Region based representation, Deformable curves and surfaces, Snakes and active contours, Level set representations, Fourier and wavelet descriptors, Medial representations, Multiresolution analysis 07
6 Hough Transform and Object recognition Line detection, Hough Transform (HT) for line detection, foot-of-normal method, line localization, line fitting, RANSAC for straight line detection, HT based circular object detection, accurate center location, speed problem, ellipse detection, Case study: Human Iris location, hole detection, generalized Hough Transform (GHT), spatial matched filtering, GHT for ellipse detection, object location, GHT for feature collation, Object Recognition: Simple object recognition methods, Shape correspondence and shape matching, Principal component analysis , Shape priors for recognition. 07
7 3D Vision and Motion Methods for 3D vision, projection schemes, shape from shading, photometric stereo, shape from texture, shape from focus, active range finding, surface representations, point-based representation, volumetric representations, 3D object recognition, 3D reconstruction, introduction to motion, triangulation, bundle adjustment, translational alignment, parametric motion, spline-based motion, optical flow, layered motion. 05
8 Applications Photo album, Face detection, Face recognition, Eigen faces, Active appearance and 3D shape models of faces Application: Surveillance, foreground-background separation, particle filters, Chamfer matching, tracking, and occlusion, combining views from multiple cameras, human gait analysis Application: In-vehicle vision system: locating roadway, road markings, identifying road signs, locating pedestrians. 04
Textbooks
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag London Limited 2011. Click Here
Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, 2003. Click Here