Image Segmentation with Shape Priors: Explicit Versus Implicit Representations

Daniel Cremers
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Daniel Cremers: Technische Universität München, Department of Computer Science

A chapter in Handbook of Mathematical Methods in Imaging, 2015, pp 1909-1944 from Springer

Abstract: Abstract Image segmentation is among the most studied problems in image understanding and computer vision. The goal of image segmentation is to partition the image plane into a set of meaningful regions. Here meaningful typically refers to a semantic partitioning where the computed regions correspond to individual objects in the observed scene. Unfortunately, generic purely low-level segmentation algorithms often do not provide the desired segmentation results, because the traditional low-level assumptions like intensity or texture homogeneity and strong edge contrast are not sufficient to separate objects in a scene. To overcome these limitations, researchers have proposed to impose prior knowledge into low-level segmentation methods. In the following, we will review methods which allow to impose knowledge about the shape of objects of interest into segmentation processes.

Keywords: Image Segmentation; Implicit Representation; Kernel Density Estimator; Shape Representation; Signed Distance Function (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4939-0790-8_40

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