BAI151A Computer Vision
Course Learning Objectives
CLO1: To understand the fundamentals of computer vision and digital image processing
CLO2: To introduce the processes involved image enhancement and restoration.
CLO3: To facilitate the students to gain understanding color image processing and morphology. CLO5: To impart the knowledge of image segmentation and object recognition techniques.
SYLLABUS COPY
MODULE - 1
Introduction : What is computer vision? A brief history. Image Formation : Photometric image formation, The digital camera. Image processing : Point operators, Linear filtering.
MODULE - 2
Image processing
More neighborhood operators, Fourier transforms, Pyramids and wavelets, and Geometric transformations.
MODULE - 3
Image Restoration and Reconstruction
A model of Image degradation/restoration process, restoration in the presence of noise only, periodic noise reduction by frequency domain filtering.
Image Segmentation
Fundamentals, Point, Line and edge detection, thresholding (Foundation & Basic global thresholding only), Segmentation by region growing & region splitting & merging.
MODULE - 4
Color Image Processing
Color fundamentals, color models, Pseudocolor image processing, full color image processing, color transformations, color image smoothing and sharpening, Using color in image segmentation, Noise in color images.
MODULE - 5
Morphological Image Processing
Preliminaries, Erosion and Dilation, opening and closing, Hit-or- miss transform, some basic morphological algorithms.
Feature Extraction
Background, Boundary preprocessing (Boundary following & Chain codes only).
Image pattern Classification: Background, Patterns and classes, Pattern classification by prototype matching (Minimum distance classifier only).
Course outcome
1. Explain the fundamentals of computer vision and its applications.
2. Apply the image enhancement techniques for smoothing and sharpening of images.
3. Compare the different image restoration and segmentation techniques.
4. Demonstrate the smoothing and sharpening techniques for color images.
5. Explain morphological, feature extraction, and pattern classification techniques for object recognition.
Suggested Learning Resources
Textbooks
1. Richard Szeliski, Computer Vision: Algorithms and Applications (Texts in Computer Science), 2nd Edition, 2022, Springer.
2. Rafael C G., Woods R E. and Eddins S L, Digital Image Processing, Pearson, 4th edition, 2019.
Reference books
1. David Forsyth and Jean Ponce, Computer Vision: A Modern Approach, 2nd Edition, Pearson, 2015.
2. Reinhard Klette, Concise Computer Vision – An Introduction into Theory and Algorithms, Springer, 2014.