Course Name: 

Computer Vision (AI354)


B.Tech (AI)


Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-0-2) 4


Introduction to Image Processing: Image Formation on Camera, Camera Mechanism, Perspective Transformation, Image Transformations, Concept of convolution, Concept of Masks, Fourier series and Transform. Features and filters: low-level vision, Linear filters, Edges and contours, Binary image analysis, Background subtraction, Texture Motion and optical flow. Grouping and fitting: mid-level vision: Segmentation and clustering algorithms, Hough transform, Fitting lines and curves, Robust fitting, RANSAC, Deformable contours, Interactive segmentation. Multiple views: Local invariant feature detection and description, Image transformations and alignment, Planar homography, Epipolar geometry and stereo, Object instance recognition, Image warphing, Image stitching, Harris corner detection-interest point detection ,SIFT descriptor, Viola Jones Face detector, Cascading Classifiers for detection. Recognition: high-level vision: Basics of Object detection and recognition, Supervised classification algorithms, Deep learning, Convolutional neural networks.


Rick Szeliski “Computer Vision: Algorithms and Applications”.
David A. Forsyth and Jean Ponce “ Computer Vision: A Modern Approach”
Linda G. Shapiro and George C. Stockman "Computer Vision”
Emanuele Trucco and Alessandro Verri Introductory Techniques for 3-D Computer Vision,.
Richard Hartley and Andrew Zisserman “Multiple View Geometry in Computer Vision”.
Richard O. Duda, Peter E. Hart, and David G. Stork” Pattern classification”.
Christopher M. Bishop “Pattern Recognition and Machine Learning”.
K. Grauman and B. Leibe “Visual Object Recognition”.


Information Technology

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National Institute of Technology Karnataka,
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