A Novel Learned Model for Boundary Detection

Ming Zheng Shao; Jian Feng Qi; Ya Chen

doi:10.4028/www.scientific.net/AMR.998-999.802

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 998-999A Novel Learned Model for Boundary Detection

A Novel Learned Model for Boundary Detection

Abstract:

Boundary detection is a critical, well-studied computer vision problem. Clearly it would be nice to have algorithms which know where one object stops and another starts. Traditional approaches look for intensity discontinuities in an image, however we believe that detecting boundary from a single image is fundamentally difficult, whereas machine learning techniques have a promising prospect on boundary detection. A novel learning model is proposed in this paper. Random decision forest classifier trained by human segmentation images will applied to determine the mapping from image patches to boundary probabilities. The results on BSDS500 dataset show that our model ties the performance of other competing approaches, but being magnitude faster than others.

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Periodical:

Advanced Materials Research (Volumes 998-999)

Pages:

802-805

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.998-999.802

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Online since:

July 2014

Authors:

Ming Zheng Shao*, Jian Feng Qi, Ya Chen

Keywords:

Boundary Detection, Classifier, Feature Extraction, Random Forest (RF)

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