An Approach For Edge Detection Based On Beamlet Transform

Edge detection is very useful and important for image processing and computer vision, as it can locate significant variations of gray images. In this paper, an algorithm based on beamlet transform is proposed to detect edges in image. Beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting, the beamlet transform is the collection of all line integrals formed by viewing the image as a piecewise constant object and integrating along line segment in the beamlet dictionary, for the maximal beamlet coefficient surviving the Canny criterion, draw a line segment depicting that beamlet, all these beamlets in different scales are fused to generate an edge map at the image pixel level. The propose method can detect lines with any orientation, location and length in different scales and avoids subjective setting. Experimental results show that the proposed method can detect edges accurately even from noise image and has a better performance. It can be suited to different images processing, in practice it has surprisingly powerful and apparently unprecedented capabilities.