Introduction-Digital Image Fundamentals: Elements of Computer Vision-Light and the Electromagnetic Spectrum-Light and the Electromagnetic Spectrum-Image Sensing and Acquisition-Image Sampling and Quantization-Some Basic Relationships between Pixels. Introduction to the Basic Mathematical Tools Used in Digital Image Processing- Intensity Transformations and Spatial

Filtering: Basic Intensity Transformation Functions-Histogram Processing-Fundamentals of Spatial Filtering -Smoothing (Lowpass) Spatial Filters-Sharpening (High pass) Spatial Filters.

Filtering in the Frequency Domain: Sampling and the Fourier Transform of Sampled Functions-Discrete Fourier Transform of One Variable-Extensions to Functions of Two Variables-Properties of the 2-D and 3-D DFT and IDFT-Filtering in the Frequency Domain-Image Smoothing Using Lowpass Frequency Domain Filters , Basics of morphological operators, Color

Image Processing: Color Fundamental-Color Models-Color Transformations-Smoothing and Sharpening-Case Studies – Medical Imaging – Security and Surveillance Systems – Automated Driving Systems.

Video processing : Digital video, 2D and 3-D Feature Detection and Matching – points and patches, tomography, Motion estimation : Motion Models, Optical flow, Matching methods

Pre-Requisite(s): None
Course Type: Lab

Course Objectives

• To explore the algorithms in spatial and frequency domain relevant to image enhancement, restoration and segmentation applications.
• To understand the binary, gray scale and color image processing with real world applications
• To understand the video representation and motion analysis tools.

Course Outcomes

CO1: Understand the mathematical foundations of image and video processing.

CO2: Apply Spatial and frequency domain filtering for image enhancement.

CO3: Understand the fundamentals of Color Image Processing

CO4: Understand the principles in video representation and motion analysis.

CO-PO Mapping

Evaluation Pattern: 70/30

Note: Continuous assessments can include quizzes, tutorials, lab assessments, case study and project reviews. Midterm and End semester exams can be a theory exam or lab integrated exam for two hours

1. Rafael C. Gonzalez,Richard E. Woods,”Digital Image Processing”, Pearson Education, 4th Edition, 2018
2. A. Murat Tekalp,”Digital Video Processing”, O’Reilly, Second Edition, 2015
3. Szeliski R. Computer Vision: Algorithms and Applications Springer. New York. 2010. https://www.cs.ccu.edu.tw/~damon/tmp/SzeliskiBook_20100903_draft.pdf