CA1 from EE5731 Computer Vision of NUS
The project is intended solely for practice and learning purposes; any other use is prohibited.
This project consists of two main computer vision tasks:
-Implement Canny edge detection and Harris corner detection from scratch
-Analyze histograms of dark (Dark-1) and bright (GT1) images
-Implement histogram equalization (HE) manually and apply to dark image
-Test feature detection on HE-enhanced image and compare results
-Implement additional enhancement method and analyze performance differences
-Compare results across original dark, HE-enhanced, custom-enhanced, and bright images
-Manually select corresponding feature points in stereo image pair (GT1 and GT2)
-Implement normalized 8-point algorithm from scratch to compute fundamental matrix
-Visualize epipolar lines for selected points
-Analyze epipolar geometry and camera position relationships
-Test epipolar constraint on new points not in the original set
-Prohibited: Using built-in functions for Canny, Harris, histogram equalization, fundamental matrix computation, or epipolar line calculation
-Required: Manual implementation of all core algorithms
-Required: Detailed analysis and discussion of results at each step
-Optional: Image conversion to grayscale or resizing with proper documentation
-Understand challenges of feature detection in low-light conditions
-Evaluate effectiveness of different image enhancement methods
-Master fundamental matrix computation and epipolar geometry principles
-Develop practical implementation skills for core computer vision algorithms