Basic topics of computer vision, and image processing -Introduce some fundamental approaches for computer vision research: Image Filtering, Edge Detection, Interest Point Detectors, Motion and Optical Flow, Object Detection and Tracking, Region/Boundary Segmentation, Shape Analysis, and Statistical Shape Models, Deep Learning for Computer Vision, Imaging Geometry, Camera Modeling, and Calibration. Recent Advances in Computer vision.

Programming:

Soft Computing: Introduction, requirement, different tools and techniques, usefulness and applications. Fuzzy Sets and Fuzzy Logic: Introduction, Fuzzy sets versus crisp sets, operations on fuzzy sets, Extension principle, Fuzzy relations and relation equations, Fuzzy numbers, Linguistic variables, Fuzzy logic, Linguistic hedges, Applications, fuzzy controllers, fuzzy pattern recognition, fuzzy image processing, fuzzy database.

Digital Image Fundamentals: Elements of visual perception–Image sampling and quantization Basic relationship between pixels– Basic geometric transformations. Image fundamentals and image restoration: Spatial domain methods‐Spatial filtering‐ Frequency domain filters –Model of Image Degradation/restoration process – Noise models – Inverse filtering ‐Least mean square filtering –Constrained least mean square filtering – Blind image restoration – Pseudo inverse – Singular value decomposition.

Introduction: speech production and perception, information sources in speech, linguistic aspect of speech, acoustic and articulatory phonetics, nature of speech ,models for speech analysis and perception; Short ‐ term processing: need, approach, time, frequency and time ‐ frequency analysis; Short ‐ term Fourier transform (STFT): overview of Fourier representation, non ‐ stationary signals, development of STFT, transform and filter ‐ bank views of STFT; cesptrum analysis: Basis and development, delta, delta ‐ delta and mel ‐ cepstrum, homomorphic signal processing, real and complex cepstru