IMAGE PROCESSING

CMSDSC716T: Image Processing and Computer Vision Theory: 45 Lectures

CMSDSC716P: CLICK HERE FOR PRACTICAL USING OPENCV 

1. Introduction (4 Lectures)

Light, Brightness adaption and discrimination, Pixels, Coordinate conventions, Imaging Geometry,

Perspective Projection, Spatial Domain Filtering, Sampling and quantization.

2. Spatial Domain Filtering: (8 Lectures)

Intensity transformations, contrast stretching, histogram equalization, Correlation and convolution,

Smoothing filters, Sharpening filters, Gradient and Laplacian.

3. Filtering in the Frequency domain: (8 Lectures)

Hotelling Transform, Fourier Transforms and properties, FFT (Decimation in Frequency and

Decimation in Time Techniques), Convolution, Correlation, 2-D sampling, Discrete Cosine Transform,

Frequency domain filtering.

4. Image Restoration: (8 Lectures)

Basic Framework, 

Interactive Restoration, 

Image deformation and geometric transformations,

 image morphing, 

Restoration techniques,

 Noise characterization, 

Noise restoration filters,

 Adaptive filters,

Linear, 

Position invariant degradations, 

Estimation of Degradation functions, 

Restoration from projections.

5. Image Compression & Segmentation: (10 Lectures)

Encoder-Decoder model:

Types of redundancies:

Lossy and Lossless compression:

 Entropy of an

information source:

 Shannon's 1st Theorem:
 Huffman Coding- CLICK HERE,

 Arithmetic Coding: , 

Run length coding.

JPEG. 

Boundary detection based techniques

, Point, line detection,

 Edge detection,

 Edge linking, 

Local processing, 

Regional processing, 

Hough transform, 

Thresholding,

 Iterative thresholding.

6. Image Description (5 Lectures)

Introduction to Computer Vision: Comparison of Image Processing, Computer Vision and Computer

Graphics, What is Computer Vision - Low-level, Mid-level, High-level processing, Overview of Diverse

Computer Vision Applications: Document Image Analysis, Biometrics, Object Recognition, Object

Tracking, Gesture Recognition, Motion Estimation