-
(Aug 30) Welcome to the CS733 class. This is a graduate class in Computer Vision and will cover a variety of topics, ranging from Projective Geometry to Pattern Recognition. The website contains a number of lectures. These slides will give you an idea of the topics we will be covering, but we will be using more material from the web and from a few books.
Course Outline
In this class we will cover the following topics:
- 1. Multiple View Geometry:
- Image Formation, Geometric aspects, Radiometric Aspects, Digital Images, The Human Eye, Camera parameters, Camera Calibration, Intrinsic Parameters, Extrinsic Parameters, Stereo, The Correspondence Problem, Epipolar Geometry, 3D Reconstruction
- 2. Edge Detection:
- Gradient based edge Detectors, Laplacian, Parametric Models
- 3. Other Image Features:
- Hough Transform, Ellipse fitting, Deformable contours
Sampling and Aliasing, Noise Reduction small
- 4. Lightness and Color:
- Surface Reflectance, Recovering Lightness, The Physics of Color, Human Color Perception, Color Representations
- 5. Filters:
- Linear Filters and Convolution, Spatial Frequency and Fourier Transform, Sampling and Aliasing, Noise Reduction
- 6. Motion and Video Processing:
- The Image Motion Field, Estimation of 3D Motion and Structure, Segmentation on the basis of different Motion, Image Compression
- 7.Shape from Single Image Cues:
- Surface Descriptions, Shape from Contours, Shape from Shading, Shape from Texture
- 8. Segmentaiton:
-
Recommended books
Multile View Geometry in Computer Vision by Hartley, R.~I. and Zisserman, A.
Robot Vision by Berthold K. P. Horn
Computer Vision: A Modern Approach by David A. Forsyth and Jean Ponce
Computer Vision: Algorithms and Applications by Richard Szeliski
Computer Vision: Models, Learning, and Inference by Simon J.D. Prince
Grading
Homework: 40%, Project: 30%, Exams: 30%
Lecture Notes
Lecture notes will appear as the semester goes on
- 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] Updated!
-
- 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)
Quizzes
Quiz 1: Transform each of the images to images of frontoparallel
surfaces, by applying an appropriate homography, as discussed
in class. Due Thursday 9/13 in class [img1] [img2]
Quiz 2: Perform an affine rectification of the image i.e. parallel lines in the scene should be parallel in the image [img1]
Quiz 3, due 11/01 [imgs]
Quiz 4, due 11/01 [imgs]
Update: you can use [this code] to stitch your images in Matlab
Homework
Homework 1, due 10/1 [pdf]
Homework 2. Use the images provided to create a 3D model of the object [seq1] [seq2]. Due before Thanksgiving.
Update: [Calibration data]
Update 2: A project on structure from motion using Tomasi Kanade factorization [link]
Project
Here's the data for the project [data]. Refer to readme file inside.
Additional Resources
Additional material will be distributed in class or posted in the web site
-
(Sep 4)
http://www.youtube.com/watch?v=q8xsXFU7dK0
http://www.dsi.unive.it/~atorsell/Visione/02-Image%20Formation.pdf
-
(Sep 6)
Dialogue 2
Notes for dialogue 2
Web Accessibility
