Introduction to Digital Image Processing

Winter 2015-2016




Class Info




Final Projects


CCNet Mirror


EE168: Introduction to Digital Image Processing Winter 2015-2016 Syllabus

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

Week 1.

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.

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.

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.

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

Week 5.

11) The fast Fourier transform

12) The convolution theorem

*13) Lab: FFTs, Image filtering: smoothing and sharpening

                        Handouts for lectures: Handout 16 Handout 19

Week 6.

14) Assignment of final project

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

*16) Lab: 2D convolution and correlation

                        Handouts for lectures: Handout 24 Handout 25

Week 7.

17) 3D information, perspective plots

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

*19) Lab: Color and color tables

                        Handouts for lectures: Handout 27

Week 8.

20) Image morphing

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

                        Handouts for lectures: Handout 30

Week 9.

22) Interpolation

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

24) False color images, principle components analysis

*25) Extra credit Lab: Principle components analysis

                        Handouts for lectures: Handout 31 Handout 33 Handout 35

Week 10.

26) Student presentations: computer animations 1

27) Student presentations: computer animations 2