Fast Detection of curved Edges at low SNR

Fast detection of Curve edge in CVPR 2016 low signal noise ratio image

Http://www.wisdom.weizmann.ac.il/~yehonato/projectPage.html

Active code

In this paper, the main idea of low signal noise ratio image curve edge detection is as follows:
1) An efficient hierarchical algorithm to examine an exponential number of candidate curved edges fast will be detected where the edges of the curve may be

Examine each potential edge curve using its "custom tailored" matched filter

Do this efficiently using a dynamic programming-like algorithm on a hierarchical, binary-split tree of the image (keep BES T curve for each and points on the boundary of a tile)

2) Noise suppression: Long is good:noise can averaged out by smoothing along the curve (while maintaining contrast across the curve ) by using a matched filter
Smoothing and noise removal on both sides of the edge

3) Use statistically rigorous adaptive threshold-detect edges at very
Low SNRs detects edges using adaptive thresholds

Computational complexity:
Stringent:o (N1.5)
Examine all contact points in the interface between tiles

Greedy:o (Nlog N)
Contact points is sorted by score; Only curves through highest scoring points is examined

runtime:0.9 (0.6) secs on 129x129