Syllabus
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Course Code: *Program Elective-V MTCE-205 Course Name: Digital Image Processing |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Introduction And Digital Image Fundamentals: The origins of Digital Image Processing, Examples of Fields that Use Digital Image Processing, Fundamentals Steps in Image Processing, Elements of Digital Image Processing Systems, Image Sampling and Quantization, Some basic relationships like Neighbours, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations. | |
| 2 | Image Enhancement in the Spatial Domain: Some 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. Image Enhancement in the Frequency Domain: Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening Frequency Domain Filters, Homomorphic Filtering. |
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| 3 | Image Restoration:A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence
of Noise Only Spatial Filtering, Pereodic Noise Reduction by Frequency Domain Filtering, Linear PositionInvarientDedradations, Estimation of Degradation Function, Inverse filtering, Wiener filtering, Constrained Least Square
Filtering, Geometric Mean Filter, Geometric Transformations. Image Compression: Coding, Interpixel and Psychovisual Redundancy, Image Compression models, Elements of Information Theory, Error free comparison, Lossy compression, Image compression standards. |
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| 4 | Image Segmentation:Detection of Discontinuities, Edge linking and boundary detection, Thresholding, Region Oriented
Segmentation, Motion based segmentation. Representation and Description: Representation, Boundary Descriptors, Regional Descriptors, Use of Principal Components for Description, Introduction to Morphology, Some basic Morphological Algorithms. Object Recoginition: Patterns and Pattern Classes, Decision-Theoretic Methods, Structural Methods. |
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