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COMPUTER VISION (BAI515A)

COMPUTER VISION

Course Code BAI515A 
CIE Marks 50
Teaching Hours/Week (L: T:P: S) 3:0:0:0 
SEE Marks 50
Total Hours of Pedagogy 40 
Total Marks 100
Credits 03 
Exam Hours 3
Examination type (SEE) Theory




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-ormiss 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).




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.

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