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(Sept 3) Welcome to CMSC426
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.
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Reading List
There is no required text. We will distribute material from a variety of sources.
Grading
Midterm: 20%, Final: 20%, Homework: 60%
Lecture Notes
- Topic 1: Image Formation 1 [pdf] [ppt]
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- Topic 2: Projective Geometry [ppt] (10 MB)
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- Topic 3: Linear Algebra Review [ppt]
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- Topic 4: Camera Calibration [pdf]
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- Topic 4: Filtering [ppt]
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- Topic 5: Edge detection [ppt]
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- Topic 6: Resampling [ppt] (Slides from Univ. of Washington)
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- Topic 7: Image motion [ppt]
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- Topic 8: Statistics on image features: Review of statistical concepts [ppt] [Website on illusions]
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- Topic 9: Stereopsis [ppt]
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- Topic 10: Epipolar Geometry [ppt] (8 MB)
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- Topic 11: Interpretation of image motion fields [ppt]
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- Topic 12: 3D motion estimation from image derivatives [ppt]
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- Topic 13: Shape from Shading [pdf] (from Daniel DeMenthon)
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- Topic 14: Texture [ppt] (5.5 MB)
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- Topic 15: Tracking with Kalman Filters [pdf] (from Daniel DeMenthon)
Homework
Homeworks will appear as the semester goes on.
All late homework submissions need to be submitted before the final exam
Additional Resources
Suggested book for reference: "Multiple view geometry in computer vision" by Hartley and Zisserman
Some slides for using MATLAB in Image Processing
Online resource of computer vision topics (contains short descriptions and tutorials on basic and advanced topics)
Image Processing Learning resources
Web Accessibility
