EC2029 - LP

7/16/2019 EC2029 - LP

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LESSON PLAN LP- EC2029

Date:

Page 01 of 06Sub Code& Name : EC2029- DIGITAL IMAGE PROCESSING

Unit: I Branch: ECE Semester:VII/A

UNIT-I DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS 9

Elements of digital image processing systems, Vidicon and Digital Camera working principles,

Elements of visual perception, brightness, contrast, hue, saturation, mach band effect, Color image

fundamentals - RGB, HSI models, Image sampling, Quantization, dither, Two-dimensional

mathematical preliminaries, 2D transforms -DFT, DCT, KLT, SVD.

Objective: To study the monochrome and color image fundamentals, mathematical transforms

necessary for image processing.

Session

NoTopics to be covered Time Ref

TeachingMethod

1. Elements of digital image processing systems 50m 1 BB

2. Vidicon and Digital Camera working principles 50m 2 BB

3. Elements of visual perception 50m 1 BB

4.Brightness, contrast, hue, saturation, mach band

effect50m 1 BB

5. Color image fundamentals - RGB, HSI models 50m 1 BB

6. Image sampling and quantization, Dither 50m 1 BB

7. Two-dimensional mathematical preliminaries 50m 2 BB

8. Two-dimensional mathematical preliminaries 50m 2 BB

9. Introduction to Fourier Transform and DFT 50m 2,4 BB

10. Discrete Cosine Transform and its properties 50m 2,4 BB

11. Karhunen – Loeve transforms and its properties 50m 2 BB

12. Singular Value Decomposition and its properties

50m

2 BB

CAT 1 50m

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Date:


Unit: II Branch: ECE Semester: VII/A

UNIT II IMAGE ENHANCEMENT 9

Histogram equalization and specification techniques, Noise distributions, Spatial averaging,

Directional Smoothing, Median, Geometric mean, Harmonic mean, Contraharmonic mean filters,

Homomorphic filtering, Color image enhancement.

Objective: To study the image enhancement techniques

SessionNo

Topics to be covered Time Ref

Teaching

Method

13.Spatial Domain methods: Basic grey level

transformation50m 1,4

BB

14.Histogram equalization Histogram specification

techniques50m

1,4BB

15. Noise Distributions 50m 1,4BB

16. Image subtraction and Image averaging 50m 1 BB

17. Smoothing, sharpening filters50m 1,4 BB

18. Geometric mean, Harmonic mean, Contraharmonicmean filters 50m 1 BB

19.Homomorphic filtering 50m

1BB

20. Color image enhancement techniques 50m 1 BB

21. Color image enhancement techniques 50m 1,4 BB

CAT-II 50m

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Date:

Page 03 of 06Sub Code& Name : EC2029-DIGITAL IMAGE PROCESSING

Unit: III Branch: ECE Semester: VII/A

UNIT III IMAGE RESTORATION 9

Image Restoration - degradation model, Unconstrained restoration - Lagrange multiplier andConstrained restoration, Inverse filtering-removal of blur caused by uniform linear motion, Wiener

filtering, Geometric transformations-spatial transformation.

Objective: To study image restoration procedures.

Session

No Topics to be covered Time Ref

Teaching

Method

22. Model of Image Degradation/restoration process 50m 1 BB

23. Noise models 50m 1 BB

24. Unconstrained restoration 50m 1 BB

25. Lagrange multiplier 50m 1 BB

26. Least mean square filtering 50m 1,4 BB

27. Constrained least mean square filtering 50m 1,3 BB

28.

Inverse filtering-removal of blur caused by uniform

linear motion

50m

1

BB

29. Wiener filtering 50m 1,4 BB

30. Geometric transformations 50m 1 BB

31. Spatial transformation50m

1BB

CAT-III50m

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Date:

Page 05 of 06Sub Code& Name : EC2029-DIGITAL IMAGE PROCESSING

Unit: IV Branch: ECE Semester: VII/A

UNIT IV IMAGE SEGMENTATION 9

Edge detection, Edge linking via Hough transform – Thresholding - Region based segmentation – Region growing – Region splitting and Merging – Segmentation by morphological watersheds –

basic concepts – Dam construction – Watershed segmentation algorithm.

Objective: To study the image segmentation procedures.

Session


Teaching

Method

32. Edge detection 50m 1 BB

33. Edge linking via Hough transform 50m 1 BB

34. Thresholding 50m 1 BB

35. Region Based segmentation 50m 1,4 BB

36. Region growing 50m 1 BB

37. Region splitting and Merging 50m 1 BB

38. Segmentation by morphological watersheds – basic concepts 50m 1,7 BB

39. Dam construction 50m 1,7 BB

40. Watershed segmentation algorithm 50m 1,7 BB

CAT-V 50m

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Date:


Unit: V Branch: ECE Semester: VII/A

UNIT V IMAGE COMPRESSION 9

Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding,Vector Quantization, Transform coding, JPEG standard, MPEG.

Objective: To study the image compression techniques.

Session


Teaching

Method

41.

Need for data compression, Different types of

compression

50m6

BB

42. Variable length coding-Huffman Coding 50m 1,3 BB

43. Tutorials 50m 1,3 BB

44. Run Length Encoding, Shift codes 50m 1 BB

45. Arithmetic coding 50m 4 BB

46. Vector Quantization 50m 4 BB

47. Lossy Compression: Transform coding 50m 1,4 BB

48. Wavelet coding 50m 1,4 BB

49. Basics of Image compression standards: JPEG 50m 1 BB

50. MPEG standards 50m 1 BB

CAT-IV 50m

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Date:

Page 06 of 06SubCode& Name : EC2029-DIGITAL IMAGE PROCESSING

Branch: ECE Semester: VII/A

Course Delivery Plan:

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

I II I II I II I II I II I II I II I II I II I II I II I II I II I II I II

Units 1 2 3 5

4

TEXTBOOKS:

1. Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Pearson, IInd

Edition, 2004.2. Anil K. Jain, “Fundamentals of Digital Image Processing”, Pearson 2002.

REFERENCES:

3. Kenneth R. Castleman, “Digital Image Processing”, Pearson, 2006.

4. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins, “Digital Image Processing using

MATLAB”, Pearson Education, Inc., 2004.

5. D. E. Dudgeon and RM. Mersereau, “Multidimensional Digital Signal Processing”, Prentice Hall

Professional Technical Reference, 1990.

6. William K. Pratt, “Digital Image Processing”, John Wiley, New York, 2002.

7. Milan Sonka et al, “Image Processing, Analysis and Machine Vision”, Brookes/Cole, Vikas

Publishing House, IInd

edition, 1999.

Prepared by Approved by

SignatureName C.AISHWARYA

Designation ASSISTANT PROFESSOR

Date

CAT I CAT II CAT III CAT IV CAT V

EC2029 - LP

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