Multiscale Edge Detection and Fiber Enhancement Using Differences of Oriented Means

We present an algorithm for edge detection suitable for both natural as well as noisy images. Our method is based on efficient multiscale utilization of elongated filters measuring the difference of oriented means of various lengths and orientations, along with a theoretical estimation of the effect of noise on the response of such filters. We use a scale adaptive threshold along with a recursive decision process to reveal the significant edges of all lengths and orientations and to localize them accurately even in lowcontrast and very noisy images. We further use this algorithm for fiber detection and enhancement by utilizing stochastic completion-like process from both sides of a fiber. Our algorithm relies on an efficient multiscale algorithm for computing all "significantly different" oriented means in an image in O(N log ), where N is the number of pixels, and is the length of the longest structure of interest. Experimental results on both natural and noisy images are ...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Meirav Galun, Ronen Basri, Achi Brandt
• Keywords: Computer Vision, Efficient Multiscale Algorithm, Efficient Multiscale Utilization, ICCV 2007, Noisy Images, Oriented Means, Recursive Decision Process