Computer Graphics and Image Processing Syllabus - BCIS (PU)

Course Objectives:

The objective of this course is to provide the knowledge of image processing and pattern recognition and apply these concepts in image processing and recognition applications of having commercial values in industry and business management. 

Course Description:

The course content is mainly focused on developing the sound theoretical foundation of all of the digital image processing stages, ranging from creation to acquisition and pre-processing to restoration. The course also requires programming assignments for deeper understanding of the various stages of image processing and pattern recognition.

Course Outcomes:

• Thorough understanding of theoretical foundation of fundamental Digital Image manipulation and processing steps like acquisition; preprocessing; segmentation; Fourier domain processing
• Skills on exploration and appropriate use of image processing methods / tools for business and management applications

Course Contents:

Unit 1: Introduction : Digital Image Processing                                        4 hours

Digital image representation, Digital image processing: Problems and applications, Elements of visual perception, Sampling and quantization, Some basic relationships like Neighbors, Connectivity, Distance, Measures between pixels, Visual Perception

Unit 2: Image Enhancement in Spatial Domain                                        4 hours

Gray Level Transformations, Histogram Processing, Enhancement Using Arithmetic and Logic operations, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters, Combining Spatial Enhancement Methods

Unit 3: Image Enhancement in the Frequency Domain                           5 Hours

Introduction to Fourier Transform and the frequency Domain, Computing and Visualizing the 2D DFT, Smoothing and Sharpening using Frequency Domain Filters, Hadamard transform, Haar transform and Discrete Cosine transform, Fast Fourier Transform

Unit 4: Image Restoration                                                                            7 Hours

The Image Degradation / Restoration Process, Noise Model based  Restoration,  Spatial filtering, Periodic Noise Reduction by Frequency Domain Filtering, Inverse filtering, Wiener filtering, Geometric Mean Filter  

Unit 5 : Color Processing                                                                             5 Hours

Color Fundamentals, Color Models, Pseudocolor based Image Processing, Color transformations, Smoothing and Sharpening operations

Unit 6: Image Compression                                                                        5 Hours

Coding, Interpixel and Psychovisual Redundancy, Image Compression models, Lossless and Lossy Compressions

Unit 7: Morphological Image Processing                                                   5 Hours

Logic Operations involving binary images, Dilation and Erosion, Opening and Closing, The Hit-or-Miss Transformation

Unit 8: Image Segmentation                                                                        5 Hours

Detection of Discontinuities, Edge linking and boundary detection, Thresholding, Region Based Segmentation

Unit 9: Pattern  Recognition                                                                        8 Hours

Descriptor concept,  Chain codes, Signatures, Shape Numbers, Fourier Descriptors,  Patterns and pattern classes, Overview of pattern recognition, Neural Network and Image Processing, NN based pattern recognition, Decision-Theoretic Pattern Recognition Methods.

Lab and Project Requirement:

This course requires extensive exposure of practical examples with at least 8-12  lab exercises with programs consisting most of topics detailed in syllabus content. Semester end, image-processing project as a course project (either individual or group (at most 4 students)  is a strict requirement for this course.

Text Book:

1. Rafael C. Gonzalez &  Richard E. Woods, “Digital Image Processing”, PHI (2010).

Reference Books:

1. A. K. Jain, “Fundamental of Digital Image processing”, PHI ( 2011).
2. P. Monique and M. Dekker, “Fundamentals of Pattern recognition”, CRC (2007).
3. M. James, “Pattern recognition”, BSP ( 2008).