Course

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.

Depth estimation and multi-camera views: Perspective, binocular stereopsis: camera and epipolar geometry; Homography, rectification, DLT, RANSAC, 3-D reconstruction framework.

Feature extraction: 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.

Image Segmentation: Region Growing, Edge Based approaches to segmentation, Graph-Cut, Mean-Shift, MRFs, Texture Segmentation; Object detection.

Pattern Analysis: Clustering: K-Means, K-Medoids, Mixture of Gaussians, Classification: Discriminant Function, Supervised, Un-supervised, Semi-supervised;

Suggested Lab Sessions:

·         Usage of OpenCV / MATLAB / Equivalent for the implementation of the course topics.

·         Develop computer vision-based applications in robotic systems.

Course Outcomes:

CO1: Understand digital image formation and related processing.

CO2: Apply depth estimation using stereo views.

CO3: Apply feature extraction techniques for detection of edges.

CO4: Perform image segmentation.

CO5: Analyze patterns using clustering and other means.

Textbooks / References:

1.      Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer-Verlag London Limited 2011.

2.      D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Pearson Education, 2003.

1. Gonzalez, Woods and Eddins, “Digital Image Processing using MATLAB”, Prentice Hall, 2004.