BIT 408




Instruction  per Week                                                                                                4 Periods
Duration of University Examination                                                                                      3 Hours
University Examination                                                                                              75 Marks
Sessional                                                                                                                     25 Marks



Image processing: Introduction, Fundamental steps, Components. Elements of visual perception, image sampling and quantization, some basic relationships between pixels.

Intensity Transformations Some Basic Intensity Transformation Functions, Histogram Processing


Spatial Filtering: Fundamentals of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters

Filtering in the Frequency Domain: Preliminary Concepts, Image Smoothing using Frequency Domain Filters, Image Sharpening Using Frequency Domain Filters.


Image Restoration and Reconstruction : A Model of the Image degradation/Restoration Process, Noise Models, Restoration in the Presence of Noise Only—Spatial Filtering, Minimum Mean Square Error (Wiener) Filtering

Morphological Image Processing: Preliminaries, Erosion and Dilation, Opening and Closing


Image Segmentation: Fundamentals, Point, Line, and Edge Detection, Segmentation by Thresholding, Region-Based Segmentation, Segmentation Using Watershed Algorithm.

Representation and Description: Representation, Some Simple Descriptors, Shape Numbers, Fourier Descriptors.

Object Recognition: Patterns and Pattern Classes, Matching: Minimum distance classifier, correlation.


Color Image Processing: Color Fundamentals, Color Models, Pseudo color Image Processing.

Image Compression: Fundamentals, Compression Techniques, Lossless Compression, Lossy Compression, Measuring Information, Lossless Compression, Huffman Encoding, Arithmetic Coding, LZW, Run Length, Predictive Coding

Suggested Reading:

1)Rafael C Gonzalez and Richard E Woods, “Digital Image Processing”, Pearson Education, 3rd Edition.

2)Vipula Singh, “Digital Image Processing with MatLab and lab View” Elsevier

3)Milan Sonka, Vaclav Halvac and Roger Boyle, “Image Processing, Analysis, and Machine Vision”, Second Edition, Thomson Learning Publishers.

4)Kenneth R.Castleman, “Digital Image Processing”, Pearson Education.

5)Rapel C Gonzalez , Richard E Woods and Steven L Eddins, “Digital Image Processing using MATLAB”, Pearson Education.


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   Thu, 24-Apr-2014, 09:39 PMDIGITAL IMAGE PROCESSING (Elective- III).
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