Computer Vision I

  • Created: 2015-10-18
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Name:Computer Vision I
No.:S081203ZJ012Semester:Spring
Hour:40Credit:2.0
Teacher:Wang, Weiqiang
Introduction:
 

Aims and Purposes: This course is targeted at graduate students who need to learn the topics and essential knowledge of computer vision. The students are expected to grasp the basic theories and paradigms of various computer vision problems, and build a solid foundation for their future thesis research work, and practice using the matlab or C, C++ to construct the various vision applications.

Prerequisite: Digital image processing, Advanced Mathematics, linear algebra, probability theory

Content:
 
Chapter 1. Cameras
Chapter 2. Geometric camera models
Chapter 3. Geometric camera calibration
Chapter 4. Radiometry-measuring light
Chapter 5. Source, shadows and shading
Chapter 6. Colour
Chapter 7. Linear filtering
Chapter 8. Edge detection
Chapter 9. Texture
Chapter 10. The geometry of multiple views
Chapter 11. Stereopsis
Chapter 12. Segmentation by clustering
Chapter 13. Segmentation by fitting a model
Chapter 14. Segmentation and fitting using probabilistic methods
Chapter 15. Tracking with linear dynamic models
Material:
 
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