Introduction to Digital Image Processing

Winter 2013-2014

 

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EE168: Introduction to Digital Image Processing Winter 2013-2014 Syllabus

The * items take place in the SCIEN Lab, Packard 021, usually on Thursdays.

Week 1. January 7

1) Introduction and organization, physics of vision, resolution, impulse response

*2) Lab: Viewing digital images, bits and bytes, raster scan format, quantization

                        Handouts for lectures: Handout 1 Handout 2

Week 2. January 14

3) Linear systems, matrix transformations, scaling, translation and rotations

*4) Lab: Scaling, translation and rotation, sums and differences

                        Handouts for lectures: Handout 4 Handout 6 Handout 7

Week 3. January 21

5) Contrast and grey levels, histograms, Gaussian and other non-linear stretches

6) Convolution, simple filters, edge detection

*7) Lab: Histograms and stretches, convolutional filters

                        Handouts for lectures: Handout 10 Handout 12

Week 4. January 28

8) The frequency domain, power spectral density, the FFT

9) Digital filtering, image enhancement, noise

*10) Lab: Fourier transforms and the frequency domain, filters

                        Handouts for lectures: Handout 15 Handout 16 Handout 19

Week 5. February 4

11) Color representation, RGB, HSI, 24 bit and 8 bit color tables

12) Storing multiple images in 8 bits, color table swaps

*13) Lab: Color basics

                        Handouts for lectures: Handout 25

Week 6. February 11

14) Midterm exam, Tuesday, Feb. 11, take out exam

15) Interpolation methods, accuracy vs. efficiency, forward and backward methods

*16) Lab: Image interpolation

                        Handouts for lectures: Handout 27

Week 7. February 18

17) Topography and shaded relief displays, contours, parallax and stereo

*18) Lab: Perspective viewing and anaglyphs

                        Handouts for lectures: Handout 30

Week 8. February 25

19) Geometric and spatial transforms, restoring distortion

20) Fitting smooth functions to sparse data, least-squares

*21) Lab: Creating multiple image sequences for the project

                        Handouts for lectures: Handout 31 Handout 33

Week 9. March 4

22) Image morphing

23) False color images, principle components analysis

*24) Extra credit Lab: Principle components analysis

                        Handouts for lectures: Handout 35

Week 10. March 11

25) More advanced topics in image processing

*26) Student presentations: computer animations 1

*27) Student presentations: computer animations 2