In this paper, we propose a method which combines Canny edge detection and steering kernel regression[5]. As the
primary features for extraction by low-level processing techniques, edges are the starting points for many computer
vision applications. During the past years, there had been many edge detection algorithms proposed. As an almost
standard framework, canny edge detector is widely used in image processing area and often checked by other algorithms
for their validity as an almost standard framework. But, due to the deficiency of using direct gradient estimation ( usually
the gray-value difference),classical canny method is vulnerable to noise. For overcoming this problem, we combine the
steering kernel estimation with the canny edge detection which taking both the spatial and radiometric information into
count simultaneously. And the experiment results perform better than classical edge detection method on detail reserving
and positioning accuracy under the effect of different kinds of noise.
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