Image  Processing & Computer Vision  :  Course  Content,            Lecture  Note, Slides,  Text books, References

 Updated  Dec. 20,  2015

The  Course  on Image Processing & Computer Vision refers to the odd semester (July–Nov) course,  title   Digital   Image Processing &  Computer Vision,  Code CS742CI3-0-2, 4  Credits,  Lectures – 42 hours,  I offered  to  the students of  7th semester B.Tech course in the  year, 2006.  The lecture  slides, about  600 numbers  in  pdf format,  have  gone  through  one  update. The  course  is  at  Jaypee  University of Engineering  and Technology (JUET),  Dept. of Computer Science &  Engineering, where  I was  Visiting  Professor. 

B.Tech Course Lectures on Image Proc. & Computer Vision, lecture notes, are under revision.

Slides, will be available after 6 months.

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Content

 

Hrs

00

Image Processing & Computer Vision :      

Course Content

 

 

01

Introduction to Digital Image Processing & Computer Vision   :    

Digital Image, Image Processing origins;  Imaging in X-Rays, ultraviolet, visible infrared, visible, microwave, and radio bands; Fundamentals of image processing;  Components of image processing systems; Glossary of terms & definitions of Low level processing, Mid level analysis, High level understanding, Pattern recognition,  Computer vision, Computer graphics.

 

1-2

02

Digital Image Fundamentals :      

Visual perception – human eye, brightness adaptation and discrimination, Electromagnetic spectrum; Image sensing and acquisition – single, strip and array sensors, Image formation models; Image sampling and quantization – basic concepts, representation of image, special and gray level resolution, aliasing, zooming and shrinking; Relationships between pixels – nearest neighbor, adjacency, connectivity, regions, and boundaries; Distance measures; Image operations on a pixel basis; Linear and nonlinear operations. 

  

3-4

03

Image Enhancement in Spatial Domain   :      

Gray level transformations - image negatives, log, power-law and piecewise linear transformation functions; Histogram processing – equalization,  matching; Enhancement operations - arithmetic, logic, subtraction and averaging; Spatial Filtering – linear & order-statistics for smoothing  and first & second derivatives/gradients  for sharpening. 

 

5-10

04

Image Enhancement in Frequency Domain :      

2-D Fourier transform, its inverse and properties; Discrete  and  Fast fourier transform; Convolution and Correlation theorems; Filtering in frequency domain - low pass smoothing, high pass sharpening, homomorphic filtering.

 

11-12

05

Image Restoration :      

Image degradation and restoration processes; Noise models - spatial properties, noise probability density functions, periodic noise, estimation of noise parameters; Restoration in the presence of noise - mean Filters, order-statistics filters, adaptive filters; Linear position-invariant degradations and estimation; Geometric Transformations - spatial transformation, gray-level interpolation.

 

13-16

06

Color Image Processing :      

Color fundamentals; Color models – RGB, CMY and HIS; Pseudocolor image processing; Full-color image processing - transformations, smoothing, sharpening, segmentation and compression.

 

17-18

07

Wavelets and Multiresolution Processing :      

Background - Image pyramids, sub-band coding, Haar transform; Multiresolution expansions - series expansions, scaling functions, wavelet functions; Wavelet transforms in one and  two dimensions; Wavelet packets.

19-20

08

Image Compression :      

Measuring information; Fundamentals of coding and inter-pixel redundancy; Image compression models – source and channel encoder/decoder;  Error-free compression using variable length, LZW, Bit-Plane, predictive lossless coding; Lossy compression using lossy predictive, transform and wavelet coding; Image compression standards.

 

21-24

09

Morphological Image Processing :      

Preliminaries - set theory and logic operations in binary images; Basic morphological operations - opening, closing operators, dilation and erosion;  Morphological algorithms - boundary extraction, region filling, extraction of connected components, convex hull, thinning, thickening, skeletons; Extension of  morphological operations to Gray-scale images.  

 

25-28

10

Image Segmentation :      

Detection of discontinuities – point, line and edges; Edge linking and boundary detection - local processing, global processing using Hough transform; Thresholding - local, global and adaptive; Region-based segmentation - region growing, region splitting and merging; Motion detection. 

 

29-36

11

Image Representation & Description :      

Representations - chain codes, polygonal approximations, signatures, boundary segments, skeletons; Boundary descriptors - shape numbers, statistical moments; Regional descriptors - topological, texture and moments of 2-D Functions.

 

37-39

12

Object Recognition :      

Patterns and pattern classes; Decision theoretic methods – matching, statistical classifiers, neural network; Structural methods - matching shape numbers, string matching, syntactic recognition of strings and trees; Need of intelligent processing and expert  systems.

 

40-42

Image  Processing  &  Computer  Vision :  Course Content , myreaders.info

Introduction to Digital Image Proc & Computer Vision

Digital Image Fundamentals : Image Proc & Computer Vision

Image Enhancement in Spatial Domain : Image Proc & Computer Vision

Image Enhancement in Frequency Domain : Image Proc & Computer Vision

Image Restoration :  Image Proc & Computer Vision

Color Image Processing : Image Proc & Computer Vision

Wavelets and Multiresolution Processing : Image Proc & Computer Vision

Image Compression : Image Proc & Computer Vision

Morphological Image Processing : Image Proc & Computer Vision

Image Segmentation : Image Proc & Computer Vision

Image Representation & Description : Image Proc & Computer Vision

Object Recognition : Image Proc & Computer Vision

Acknowledgments

In the preparation of the course material any quote, paraphrase or summary,  information, idea, text, data, table, figure or any other material which originally appeared in someone else’s work, I sincerely acknowledge them.
 

Recommended Textbooks

 

 

References

 

 

 

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