Computer Graphics and Image Processing Syllabus - BCIS (PU)
View and download full syllabus of Computer Graphics and Image Processing
Course Description
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
Unit Contents
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 and Reference Books
Text Book:
- Rafael C. Gonzalez & Richard E. Woods, “Digital Image Processing”, PHI (2010).
Reference Books:
- A. K. Jain, “Fundamental of Digital Image processing”, PHI ( 2011).
- P. Monique and M. Dekker, “Fundamentals of Pattern recognition”, CRC (2007).
- M. James, “Pattern recognition”, BSP ( 2008).
- Short Name CGIP
- Course code CMP 362
- Semester Sixth Semester
- Full Marks 100
- Pass Marks 45
- Credit 3 hrs
- Elective/Compulsary Compulsary