DIGITAL IMAGE PROCESSING AND COMPUTER VISION

Course Code: BTC 801

Credit Units: 03

Course aim:To introduce basic principles of Digital Image Processing and Computer Vision techniques and to lay the theoretical foundation of image processing and computer vision theory for developing applications involving digital image processing and computer vision. Students successfully completing this course will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications.

Course Contents:Module I: Introduction and Digital Image Fundamentals

The origins of Digital Image Processing, Fundamentals Steps in Image Processing, Elements of Digital Image Processing Systems, Image Sampling and Quantization, Some basic relationships like Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.

Module II: Image Enhancement in the Spatial and Frequency Domain

Basic Gray Level Transformations, Histogram Processing, Enhancement Using Arithmetic and Logic operations, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters, Combining Spatial Enhancement Methods.Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening Frequency Domain Filters, Homomorphic Filtering.

Module III: Image Restoration

Model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise Only Spatial Filtering, Periodic Noise Reduction by Frequency Domain Filtering, Linear Position- Invariant Degradations, Estimation of Degradation Function, Inverse filtering, Wiener filtering, Constrained Least Square Filtering, Geometric Mean Filter, Geometric Transformations.

Module IV: Image Compression and Image Segmentation

Coding redundancy(Huffman encoding & Decoding), Image Compression models, Elements of Information Theory, Error free comparison, Lossy compression, Image compression standards, Fast Wavelet Transform; Inverse Wavelet Transform: JPEG. Detection of Discontinuities, Edge linking and boundary detection, Threshold, Region Oriented Segmentation, Motion based segmentation.

Module V: Morphological Operations and Computer Vision Applications

Representation, Boundary Descriptors, Regional Descriptors, Introduction to Morphology, Some basic Morphological Algorithms. Patterns and Pattern Classes, Decision-Theoretic Methods, Structural Methods. Camera geometry & stereo Imaging, Image registration, Review of Computer Vision applications; Fuzzy-Neural algorithms for computer vision applications

Examination Scheme:

Components CT H V A EEWeightage (%) 10 7 8 5 60

Text & References:

Text: Rafael C. Conzalez & Richard E. Woods, “Digital Image Processing”, 2nd edition, Pearson Education. A. K. Jain, “Fundamental of Digital Image Processing”, PHI. David A. Forsyth, Jean Ponce, “Computer Vision: A Modern Approach”, Prentice Hall

References: Rosefield Kak, “Digital Picture Processing”, W.K. Pratt, “Digital Image Processing” J.R. Parker, “Algorithms for Image Processing and Computer Vision,” John Wiley Sons