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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Investigating how and when perceptual organization cues improve boundary detection in natural images
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Leandro A. Loss, Computer Vision Laboratory, University of Nevada, Reno, USA
George Bebis, Computer Vision Laboratory, University of Nevada, Reno, USA
Mircea Nicolescu, Computer Vision Laboratory, University of Nevada, Reno, USA
Alexei Skurikhin, Space and Remote Sensing Sciences, Los Alamos National Laboratory, USA
Boundary detection in natural images represents an important but also challenging problem in computer vision. Motivated by studies in psychophysics claiming that humans use multiple cues for segmentation, several promising methods have been proposed which perform boundary detection by optimally combining local image measurements such as color, texture, and brightness. Very interesting results have been reported by applying these methods on challenging datasets such as the Berkeley segmentation benchmark. Although combining different cues for boundary detection has been shown to outperform methods using a single cue, results can be further improved by integrating perceptual organization cues with the boundary detection process. The main goal of this study is to investigate how and when perceptual organization cues improve boundary detection in natural images. In this context, we investigate the idea of integrating with segmentation the Iterative Multi-Scale Tensor Voting (IMSTV), a variant of Tensor Voting (TV) that performs perceptual grouping by analyzing information at multiple-scales and removing background clutter in an iterative fashion, preserving salient, organized structures. The key idea is to use IMSTV to post-process the boundary posterior probability (PB) map produced by segmentation algorithms. Detailed analysis of our experimental results reveals how and when perceptual organization cues are likely to improve or degrade boundary detection. In particular, we show that using perceptual grouping as a post-processing step improves boundary detection in 84% of the grayscale test images in the Berkeley segmentation dataset.
Citation:
Leandro A. Loss, George Bebis, Mircea Nicolescu, Alexei Skurikhin, "Investigating how and when perceptual organization cues improve boundary detection in natural images," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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