文件名称:fast_cpda
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一种快速的基于在弦到点距离累技术的角点检测- A Fast Corner Detector Based on the Chord-to-Point Distance Accumulation Technique
1. Find the edge image using the Canny edge detector.
2. Extract edges (curves) from the edge image:
2a. fill gaps if they are within a range and select long edges,
2b. find T-junctions and mark them as T-corners.
2c. obtain the `status of each selected edge ${\Gamma}$ as either `loop or `line .
3. Smooth ${\Gamma}$ using a small width Gaussian kernel in order to remove quantization noises and trivial details. This small scale Gaussian smoothing also offers good localization of corners.
4. Select significant points on the smoothed curve using scale evolution technique.
5. At each selected point of the smoothed curve, compute three discrete curvatures following the CPDA technique using three chords of different lengths.
6. Find three normalized curvatures at each selected point of and then multiply them to obtain the curvature product.
7. Find the local maxima of
1. Find the edge image using the Canny edge detector.
2. Extract edges (curves) from the edge image:
2a. fill gaps if they are within a range and select long edges,
2b. find T-junctions and mark them as T-corners.
2c. obtain the `status of each selected edge ${\Gamma}$ as either `loop or `line .
3. Smooth ${\Gamma}$ using a small width Gaussian kernel in order to remove quantization noises and trivial details. This small scale Gaussian smoothing also offers good localization of corners.
4. Select significant points on the smoothed curve using scale evolution technique.
5. At each selected point of the smoothed curve, compute three discrete curvatures following the CPDA technique using three chords of different lengths.
6. Find three normalized curvatures at each selected point of and then multiply them to obtain the curvature product.
7. Find the local maxima of
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fast_cpda.m