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

Keywords: Thinning algorithm Robustness against noise Scale space filtering Sketch image preprocessing a b s t r a c t 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. Thinning algorithms are classic in digital image processing and used to extract a pattern's skeleton, that is a thin or nearly thin representation of the pattern [1]. Thinning algorithms have been used in many applications such as OCR [2,3], document image analysis [4,5], fingerprint identification [6,7,8], biometric authentication using retinal images [9,10], signature verification [11–13], sketch matching and sketch-based image retrieval [14–17], etc. In OCR, thinning is used as a normalization step to insure invariance to pen thickness and handwriting styles. In fingerprint identification, thinning is used as a key preprocessing step essential before feature extraction. In authentication systems using retinal images, thinning is applied in order to produce a one-pixel-wide vascular tree of blood vessels, whose geometrical and topological properties are used in the identification process. In document analysis, signature verification and sketch-based image retrieval, thinning is used as a preprocessing and normalization step. A thinning algorithm is considered desirable if it meets the following properties [1,18]: produce a thin or nearly thin skeleton; preserve the connectivity of the original pattern, which means that connected parts in the original pattern should stay connected in the skeleton; preserve the visual topology of the original pattern, which means that although the skeleton is a compact representation of the original pattern, it should deliver …