Using scale space filtering to make thinning algorithms robust against noise in sketch images

We apply scale space filtering to thinning of binary sketch images by introducing a framework for making thinning algorithms robust against noise. Our framework derives multiple representations of an input image within multiple scales of filtering. Then, the filtering scale that gives the best trade-off between noise removal and shape distortion is selected. The scale selection is done using a performance measure that detects extra artifacts (redundant branches and lines) caused by noise and shape distortions introduced by high amount of filtering. In other words, our contribution is an adaptive preprocessing, in which various thinning algorithms can be used, and which task is to estimate automatically the optimal amount of filtering to deliver a neat thinning result. Experiments using five state-of-the-art thinning algorithms, as the framework’s thinning stage, show that robustness against various types of noise was achieved. They are mainly contour noise, scratch, and dithers. In addition, application of the framework in sketch matching shows its usefulness as a preprocessing and normalization step that improves matching performances.