Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46776
Title: Distance maps from unthresholded magnitudes
Authors: Antón-Canalís, Luis
Hernández Tejera, Mario 
Sánchez-Nielsen, Elena
UNESCO Clasification: 1203 Ciencia de los ordenadores
120304 Inteligencia artificial
Keywords: Distance map
Distance transform
Pseudodistance
Issue Date: 2012
Project: Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. 
Journal: Pattern Recognition 
Abstract: A straightforward algorithm that computes distance maps from unthresholded magnitudes is presented, suitable for still images and video sequences. While results on binary images are similar to classic Euclidean Distance Transforms, the proposed approach does not require a binarization step. Thus, no thresholds are needed and no information is lost in intermediate classification stages. Experiments include the evaluation of spatial and temporal coherence of distance map values, showing better results in both measurements than those obtained with Sobel or Deriche gradients and classic chessboard distance transforms.
URI: http://hdl.handle.net/10553/46776
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2012.02.010
Source: Pattern Recognition [ISSN 0031-3203], v. 45 (9), p. 3125-3130
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