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image edge detection; range image; probability density function; surface; probabilistic framework; time-of-flight camera; kinect
Abstract :
[en] We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge detection, feature extraction, and classification. We use the geometrical nature of the data to derive an analytic expression for the joint probability density function (pdf) for the random variables used to model the ranges of a set of pixels in a local neighborhood of an image. We decompose this joint pdf by considering independently the cases where two real world points corresponding to two neighboring pixels are locally on the same real world surface or not. In particular, we show that this joint pdf is linked to the Voigt pdf and not to the Gaussian pdf as it is assumed in some applications. We apply our framework to edge detection and develop a locally adaptive algorithm that is based on a probabilistic decision rule. We show in an objective evaluation that this new edge detector performs better than prior art edge detectors. This proves the benefits of the probabilistic characterization of the local neighborhood as a tool to improve applications that involve range images.
Research Center/Unit :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège Telim
Disciplines :
Computer science
Author, co-author :
Lejeune, Antoine ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images
Verly, Jacques ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images
Van Droogenbroeck, Marc ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
Probabilistic Framework for the Characterization of Surfaces and Edges in Range Images, with Application to Edge Detection
Publication date :
September 2018
Journal title :
IEEE Transactions on Pattern Analysis and Machine Intelligence
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