No abstract

Computing skeletons of 2D shapes, and medial surface and curve skeletons of 3D shapes, is a challenging task. In particular, there is no unified framework that detects all types of skeletons using a single model, and also produces a multiscale representation which allows to progressively simplify, or regularize, all skeleton types. In this paper, we present such a framework. We model skeleton detection and regularization by a conservative mass transport process from a shape's boundary to its surface skeleton, next to its curve skeleton, and finally to the shape center. The resulting density field can be thresholded to obtain a multiscale representation of progressively simplified surface, or curve, skeletons. We detail a numerical implementation of our framework which is demonstrably stable and has high computational efficiency. We demonstrate our framework on several complex 2D and 3D shapes.

In recent years, many new methods have been proposed for extracting curve skeletons of 3D shapes, using a mesh-contraction principle. However, it is still unclear how these methods perform with respect to each other, and with respect to earlier voxel-based skeletonization methods, from the viewpoint of certain quality criteria known from the literature. In this study, we compare six recent contraction-based curveskeletonization methods that use a mesh representation against six accepted quality criteria, on a set of complex 3D shapes. Our results reveal previously unknown limitations of the compared methods, and link these limitations to algorithmic aspects of the studied methods.

Abstract. We propose a new approach to digital hair removal from dermoscopic images, based on a threshold-set model. For every threshold, we adapt a recent gapdetection algorithm to find hairs, and merge results in a single mask image. Next, we find hairs in this mask by combining morphological filters and medial descriptors. We derive robust parameter settings for our method based on its application on over 300 skin images. We detail a GPU implementation of our method and show how it compares favorably with five existing digital hair removal methods.

We propose a method for digital hair removal from dermoscopic images, based on a threshold-set model. For every threshold, we adapt a recent gap-detection algorithm to find hairs, and merge results in a single mask image.We find hairs in this mask by combining morphological filters and medial descriptors.We derive robust parameter values for our method from over 300 skin images.We detail a GPU implementation of our method and show how it compares favorably with five existing hair removal methods, in terms of removing both long and stubble hair of various colors, contrasts, and curvature. We also discuss qualitative and quantitative validations of the produced hair-free images, and show how our method effectively addresses the task of automatic skin-tumor segmentation for hair-occluded images.