1. Introduction:

What is Computer Vision? Ongoing Research and Application Areas.

2. Image Formation:

Geometric aspects, Radiometric Aspects, Digital Images, The Human Eye, Camera parameters

3. Filters:

Linar Filters and Convolution, Spatial Frequency and Fourier Transform, Sampling and Aliasing, Noise Reduction small

4. Edge Detection:

Gradient based edge Detectors, Laplacian, Parametric Models

5. Other Image Features:

Hough Transform, Ellipse fitting, Deformable contours

6. Lightness and Color:

Surface Reflectance, Recovering Lightness, The Physics of Color, Human Color Perception, Color Representations

7.Camera Calibration :

Intrinsic Parameters, Extrinsic Parameters

8. Multiple View Geometry:

Stereo, The Correspondence Problem, Epipolar Geometry, 3D Reconstruction

9. Motion:

The Image Motion Field, Estimation of 3D Motion and Structure, Segmentation on the basis of different Motion, Image Compression

10. Shape from Single Image Cues:

Surface Descriptions, Shape from Contours, Shape from Shading, Shape from Texture.

 

Topic 1: Introduction [pdf]

 

Topic 2: Image Formation 1 [pdf] [ppt]

 

Topic 3: Linear Algebra Review [Linear Albebra Tutorial]

 

Topic 4: Filtering [ppt1] [ppt2]

 

Topic 5: Edge detection [ppt] Canny edge detection [m-file]

 

Topic 6: Resampling [ppt] (Slides from Univ. of Washington) 

 

Topic 7: Image motion [ppt] 

 

Topic 8: Statistics on image features: Review of statistical concepts [ppt] [Website on illusions] 

 

Topic 9: Stereopsis [ppt] 

 

Topic 10: Projective Geometry [ppt] (10 MB) 

 

Topic 11: Epipolar Geometry [ppt] (8 MB) 

 

Topic 12: Interpretation of image motion fields [ppt] 

 

Topic 13: 3D motion estimation from image derivatives [ppt] 

 

Topic 14: Shape from Shading [pdf] (from Daniel DeMenthon)

 

Topic 15: Texture [ppt] (5.5 MB)

 

Topic 16: Tracking with Kalman Filters [pdf] (from Daniel DeMenthon)