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We introduce an edge detection and recovery framework based on open activecontour models (snakelets). This is motivated by the noisy or broken edgesoutput by standard edge detection algorithms, like Canny. The idea is toutilize the local continuity and smoothness cues provided by strong edges andgrow them to recover the missing edges. This way, the strong edges are used torecover weak or missing edges by considering the local edge structures, insteadof blindly linking them if gradient magnitudes are above some threshold. Weinitialize short snakelets on the gradient magnitudes or binary edgesautomatically and then deform and grow them under the influence of gradientvector flow. The output snakelets are able to recover most of the breaks orweak edges, and they provide a smooth edge representation of the image; theycan also be used for higher level analysis, like contour segmentation.

Active Canny: Edge Detection and Recovery with Open Active Contour Models