The intent of this course is to familiarize the students to explain the fundamental concepts/issues of Computer Vision and Image Processing, and major approaches that address them. This course provides an introduction to computer vision including image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and selection for pattern classification/recognition, and advanced concepts like motion estimation and tracking, image classification, scene understanding, object classification and tracking, image fusion, and image registration, etc. This course will cover the fundamentals of Computer Vision. It is suited for mainly students who are interested in doing research in the area of Computer Vision. After completing the course, the students may expect to have the knowledge needed to read and understand more advanced topics and current research literature, and the ability to start working in industry or in academic research in the field of Computer Vision and Image Processing. They can also apply all these concepts for solving the real-world problems. INTENDED AUDIENCE :UG, PG and Ph.D students. PREREQUISITES : Basic co-ordinate geometry, matrix algebra, linear algebra and random process. INDUSTRIES SUPPORT :The software industries that develop computer visions apps would be benefitted from this course.
Computer Vision And Image Processing - Fundamentals And Applications
Indian Institute of Technology Guwahati and NPTEL via Swayam
This course may be unavailable.
Overview
Syllabus
Week 1: Introduction to Computer Vision and Basic Concepts of Image Formation: Introduction and Goals of Computer Vision and Image Processing, Image Formation Concepts.
Week 2: Fundamental Concepts of Image Formation: Radiometry, Geometric Transformations, Geometric Camera Models.
Week 3: Fundamental Concepts of Image Formation: Camera Calibration, Image Formation in a Stereo Vision Setup, Image Reconstruction from a Series of Projections.
Week 4: Image Processing Concepts: Image Transforms.
Week 5: Image Processing Concepts: Image Transforms, Image Enhancement.
Week 6: Image Processing Concepts: Image Filtering, Colour Image Processing, Image Segmentation
Week 7: Image Descriptors and Features: Texture Descriptors, Colour Features, Edges/Boundaries.
Week 8: Image Descriptors and Features: Object Boundary and Shape Representations.
Week 9: Image Descriptors and Features: Interest or Corner Point Detectors, Histogram of Oriented Gradients, Scale Invariant Feature Transform, Speeded up Robust Features, Saliency
Week 10: Fundamentals of Machine Learning: Linear Regression, Basic Concepts of Decision Functions, Elementary Statistical Decision Theory, Parameter Estimation, Clustering for Knowledge Representation, Dimension Reduction, Linear Discriminant Analysis.
Week 11: Applications of Computer Vision: Artificial Neural Network for Pattern Classification, Convolutional Neural Networks, Autoencoders.
Week 12: Applications of Computer Vision: Gesture Recognition, Motion Estimation and Object Tracking, Programming Assignments.
Week 2: Fundamental Concepts of Image Formation: Radiometry, Geometric Transformations, Geometric Camera Models.
Week 3: Fundamental Concepts of Image Formation: Camera Calibration, Image Formation in a Stereo Vision Setup, Image Reconstruction from a Series of Projections.
Week 4: Image Processing Concepts: Image Transforms.
Week 5: Image Processing Concepts: Image Transforms, Image Enhancement.
Week 6: Image Processing Concepts: Image Filtering, Colour Image Processing, Image Segmentation
Week 7: Image Descriptors and Features: Texture Descriptors, Colour Features, Edges/Boundaries.
Week 8: Image Descriptors and Features: Object Boundary and Shape Representations.
Week 9: Image Descriptors and Features: Interest or Corner Point Detectors, Histogram of Oriented Gradients, Scale Invariant Feature Transform, Speeded up Robust Features, Saliency
Week 10: Fundamentals of Machine Learning: Linear Regression, Basic Concepts of Decision Functions, Elementary Statistical Decision Theory, Parameter Estimation, Clustering for Knowledge Representation, Dimension Reduction, Linear Discriminant Analysis.
Week 11: Applications of Computer Vision: Artificial Neural Network for Pattern Classification, Convolutional Neural Networks, Autoencoders.
Week 12: Applications of Computer Vision: Gesture Recognition, Motion Estimation and Object Tracking, Programming Assignments.
Taught by
Prof. M. K. Bhuyan
