The course will have a comprehensive coverage of theory and computation related to imaging geometry, and scene understanding. It will also provide exposure to clustering, classification and deep learning techniques applied in this area. INTENDED AUDIENCE: Computer Science and Engineering, Electronics Engineering, Electrical Engineering PRE-REQUISITES: Liner Algebra, Vector Calculus, Data Structures and Programming
Overview
Syllabus
Week 1: Fundamentals of Image processing Week 2: 2-D Projective Geometry, homography, and Properties of homography
Week 3: Camera geometry
Week 4: Stereo geometry
Week 5: Stereo geometryWeek 6: Feature detection and descriptionWeek 7: Feature matching and model fittingWeek 8: Color processing Week 9: Range image processingWeek 10: Clustering and classificationWeek 11: Dimensionality reduction and sparse representation Week 12: Deep neural architecture and applications
Week 3: Camera geometry
Week 4: Stereo geometry
Week 5: Stereo geometryWeek 6: Feature detection and descriptionWeek 7: Feature matching and model fittingWeek 8: Color processing Week 9: Range image processingWeek 10: Clustering and classificationWeek 11: Dimensionality reduction and sparse representation Week 12: Deep neural architecture and applications
Taught by
Prof. Jayanta Mukhopadhyay
