FACULTY OF ENGINEERING AND TECHNOLOGY

Transcription

1 FACULTY OF ENGINEERING AND TECHNOLOGY SCHOOL OF COMPUTING DEPARTMENT OF CSE COURSE PLAN Course Code : CS0323 (Elective) Course Title : DIGITAL IMAGE PROCESSING Semester : V Course Time : JULY 2014 DEC 2014 Day Hour Timing 1 2 & am & pm & pm & pm Location Faculty Details : Tech Park (8 th Floor) Sec. Name Office Tech Park,806A A,B,C,D,E Dr. D. MALATHI Tech Park,803A F,G,H,I,J Mrs.G.NIRANJANA Office hour Mon - Fri Mon - Fri Mail id malathi.d@ktr.srmuniv.ac.in niranjana.g@ktr.srmuniv.ac.in TEXT BOOK 1. Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing", 3rd edition, Pearson Education, 2012 REFERENCE BOOKS 1. S.Annadurai, R.Shanmugalakshmi, "Fundamentals of Digital Image Processing", Pearson Education, William Pratt, "Digital Image Processing", Wiley Interscience, 2nd edition Anil Jain K. Fundamentals of Digital Image Processing, PHI Learning Pvt. Ltd., S.Jayaraman, S. Esaki Rajan, T.Veera Kumar, Digital Image Processing, Tata McGraw Hill Pvt. Ltd., Bhabatosh Chanda, Dwejesh Dutta Majumder, Digital Image Processing and analysis, Second Edition, PHI Learning Pvt. Ltd.,

2 6. Malay K.Pakhira, Digital Image Processing and Pattern Recognition, PHI Learning Pvt. Ltd., Web resources Prerequisite : Objectives : MA02011 In this course, students will learn the following topics: Image fundamentals and techniques To learn various Image enhancement, restoration and compression techniques To learn various Image segmentation, representation and description methods Assessment Details Cycle Test I : 10 Marks Cycle Test II : 10 Marks Model Examination : 20 Marks Surprise Test : 05 Marks Attendance : 05 Marks Test Schedule S.No. DATE TEST TOPICS DURATION 1 As per Cycle Test - I Unit I & II(Half) 2 periods 2 As per Cycle Test - II Unit II (Half) Unit III & IV 2 periods 3 As per Model Exam All 5 units 3 Hrs Outcomes Students who have successfully completed this course will have full understanding of the following concepts Course outcome To learn about Basic knowledge about Digital image processing Image Enhancement Image Restoration Image Compression Image Segmentation, Representation and Program outcome Ability to understand the basic concepts of Digital Image Processing Ability to develop Techniques for Image Enhancement Ability to compress images Ability to apply the techniques to segment the images - 2 -

3 Description Ability to use image processing techniques for suitable applications Detailed Session Plan INTRODUCTION Origin of Digital Image processing - fundamental steps - Components of Image processing system - Visual perception - Light and EM spectrum - Image sensing and acquisition - Image sampling and Quantization - relationship between pixels. Session No. Topics to be covered Time Teaching Ref (min) Method Testing Method 1 Origin of Digital Image processing BB/PP,Quiz 2 Fundamental steps in digital image processing 3 Components of Image processing System 4 Visual perception & Light and EM spectrum 5 Image sensing and acquisition BB/PP 6 Image sampling & Image quantization BB/PP 7 Relationship between pixels BB/PP IMAGE ENHANCEMENT Spatial Domain: Gray level transformation - Histogram processing - Arithmetic / Logic operations- Spatial filtering - smoothing filters - sharpening filters Frequency Domain: Fourier transform - smoothing frequency domain filters - sharpening filters - Homographic filtering. 8 Gray level transformation BB/PP, Quiz 9 Histogram processing BB/PP 10 Arithmetic / Logic operations- BB/PP Comparative Study 11 Spatial filtering 12 smoothing filters 13 Sharpening Filters, Assignment 14 Fourier Transform, Group discussion 15 Frequency Domain BB/PP, Quiz 16 Smoothing Frequency Domain BB/PP 17 Sharpening Filters BB/PP Comparative Study 18 Homographic filtering IMAGE RESTORATION Model of Image degradation/ restoration process - Noise models - mean filters - order statistics - adaptive filters - band reject - bandpass - notch - optimum notch filters - Linear, position invariant degradations - establishing degradation functions - Inverse filtering - Weiner - least square - Geometric mean filters.

4 19 Model of Image degradation/ restoration T1 50 process, Noise models BB 20 Mean Filters 50 T1 21 Order Statistics 50 T1 BB Quiz 22 Adaptive filtes 50 T1 BB 23 Band Reject, Bandpass, 50 T1 24 Notch filters and optimum notch filters 50 T1 25 Linear, Position invariant degradations 26 Estimating degrading functions 27 Inverse and Weiner Filtering 28 Least square and Geometric mean filters BB/PP IMAGE COMPRESSION Fundamentals - Image compression models - Information theory - error free compression: variable length - LZW - Bitplane - Lossless predictive coding; Lossy compression : Lossy predictive - transform - wavelet coding; Image compression standards. 29 Fundamentals of image compression T1 BB 30 Image compression models 31 Information theory 50 T1 32 Error Free Compression, Variable Length Coding 33 Bitplane coding 50 T1 34 Lossless Predictive coding 50 T1 BB/PP 35 Lossy Compression: Lossy predictive coding 36 Transform coding 50 T1 37 Wavelet coding 38 Compression standards 50 T1 BB/PP Comparative study IMAGE SEGMENTATION, REPRESENTATION & DESCRIPTION Segmentation: Detection of discontinuities - Edge linking & Boundary detection - Thresholding - region based segmentation Representation & Description: Chain codes - Polygonal approximations - signatures - Boundary segments - Skeletons; Boundary Descriptors - Regional descriptors 39 Segmentation: Detection of discontinuities 50 T1 BB, Quiz 40 Edge linking, Boundary detection 50 T1 BB Quiz 41 Thresholding 42 Region based segmentation 50 T1 BB/PP Objective Type Test 43 Chain codes: Polygonal approximations, Signatures, 44 Boundary segments, Skeletons 45 Boundary and Regional Descriptors BB Black Board PP Power Point

5 Prepared By Dr. D. Malathi, Prof/CSE Approved by HOD/CSE - 5 -