Syllabus

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.

Multi‐resolution Analysis and Compression: Multi Resolution Analysis: Image Pyramids – Multi resolution expansion–Wavelet Transforms. Image compression: Fundamentals Elements of Information Theory–Error free compression–Lossy Compression–Compression Standards. Wavelet coding–Basics of Image compression standards: JPEG, MPEG, Basics of Vector quantization.

Image Segmentation and Image Analysis: Edge detection–Thresholding‐Region Based segmentation – Boundary representation: boundary descriptors: Texture, Motion image analysis. Color Image Processing–Color Models‐Color Image enhancement‐Segmentation Object Recognition and Image Understanding: Patterns and pattern classes ‐ Decision‐Theoretic methods ‐ Structural methods‐3D Vision.