摘要：The thinning algorithm is one of the fastest approaches to extract skeletons from an object, especially when adopting the parallel strategy. Skeletons are very useful descriptors and can be applied in many recognition fields. However, one of the drawbacks that limits the use of these techniques is that thinning algorithms are not robust against inner noise and outer noise, which may produce many unwanted branches. To alleviate the influence of noise and increase the robustness, pruning methods and scale-space methods have been proposed in the past, in which pruning methods are aimed at suppressing the outer noise (boundary noise) and scale-space methods are aimed at suppressing the inner noise (such as scratch noise and dithering noise). In this paper, we proposed an improved framework that can deal with both inner noise and outer noise. The experiment proved that the proposed framework has better visual effects than the existing pruning method and existing scale-space method. In addition, the proposed framework is an adaptive framework that does not require manual tuning of parameters.
关键词：Adaptive framework;Thinning algorithm;Robustness against noise;pruning algorithm;scale-space filtering
