CMSC 426

Image Processing

(Computer Vision)

Instructor :         Cornelia Fermueller,  fer@cfar.umd.edu

Location and Time : Tu, Thu : 12:30 - 1:45 at CSI 3118

Office hours :          Tu, Thu :  1:45 - 2:45  (AVW 4459)

 Text :                     Introductory Techniques for 3-D Computer Vision, E. Trucco and A. Verri, Prentice Hall

Course Outline

In this class we will cover the following topics:

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.

 

 

Lecture Notes

Lecture 1: Intoduction  pdf

Lecture 2: Image formation 1  pdf   ppt

Lecture 3: Image formation 2: Radiometry  pdf   ppt

Lecture 4: Linear algebra review and Introduction to Matlab   Linear algebra tutorial (from PennState)  Matlab script 1  Matlab script 2 (some image operations)

Lectures 5 and 6: Camera Calibration  ppt  Some linear algebra for solving equations ppt 

Lecture 7: Filtering   filter.ppt   filter2.ppt 

Lectures 8 and 9 and some of 10: Edge detection   ppt   Canny edge detection m-file 

Lecture 10: Resampling   ppt   (Slides from Univ. of Washington) 

Lecture 11: Image motion   ppt  

Lecture 12: Statistics on image features:   Review of statistical concepts ppt   Web site on illusions  

Lecture 13: Stereopsis   ppt  

Lecture 14: Projective Geometry   ppt  (10 MB ) 

Lectures 15 and 18: Epipolar Geometry   ppt  (8 MB ) 

Lectures 19 and 20: Interpretation of image motion fields   ppt  

Lecture 21: 3D motion estimation from image derivatives   ppt  

Lecture 23: Shape from Shading   pdf   (from Daniel DeMenthon)

Lecture 24: Texture   ppt   (5.5 MB)

Lecture 25: Tracking with Kalman Filters   pdf   (from Daniel DeMenthon)

Homework

Homework 1  pdf   Images: 1   2   3   4   5    data file: data1.mat

Homework 2  pdf   Images: office-scene.zip   Optical flow comparison paper (Barron et. al. 1994) 

If you have an older version of matlab here are routines for reading ppm files.

Homework 3  pdf   Stereo paper (Scharstein Szelisky) 

Grading: 

Midtem ( pdf )   30%,  Final 30 %,   Homework 40 %

Useful   Links:    

    Online resource of computer vision topics: (contains short descriptions and tutorials on basic and advanced topics)      
           http://www.dai.ed.ac.uk/CVonline/       

    Image Processing Learning resources:
           http://www.dai.ed.ac.uk/HIPR2/index.htm