The Canny Edge detector was developed by John F. Canny in 1986. Also known to many as the optimal detector, Canny algorithm aims to satisfy three main criteria:
- Low error rate: Meaning a good detection of only existent edges.
- Good localization: The distance between edge pixels detected and real edge pixels have to be minimized.
- Minimal response: Only one detector response per edge
Canny Edge Detection is based on the following steps:
1.Grayscale Conversion
2.Noise Reduction
3.Determining Intensity Gradients
4.Non-Maximum Suppression
5.Edge Tracking by Double Threslholding Hysteresis
- https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_canny/py_canny.html
- http://justin-liang.com/tutorials/canny/
- https://medium.com/@ssatyajitmaitra/what-canny-edge-detection-algorithm-is-all-about-103d94553d21
- https://www.ijcsi.org/papers/IJCSI-9-5-1-269-276.pdf
- https://aishack.in/tutorials/canny-edge-detector/
- https://github.com/MadhavEsDios/Canny-Edge-Detector
- https://www.hackerearth.com/practice/notes/thethunder666/canny-edge-detection/
- https://towardsdatascience.com/canny-edge-detection-step-by-step-in-python-computer-vision-b49c3a2d8123