publication . Conference object . Other literature type . 2013

structured forests for fast edge detection

Dollar, Piotr; Zitnick, C. Lawrence;
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  • Published: 01 Dec 2013
  • Publisher: IEEE
Abstract
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. Th...
Subjects
free text keywords: Decision tree, Image segmentation, Structured prediction, Computer vision, Segmentation, Detector, Scale-space segmentation, Scale space, Computer science, Artificial intelligence, business.industry, business, Edge detection, Pattern recognition
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