Structured Forests for Fast Edge Detection

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 pa...

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Bibliographic Details
Main Authors: Dollar, Piotr, Zitnick, C. Lawrence
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Computer vision
Decision trees
Detectors
Edge detection
Image color analysis
Image edge detection
Image segmentation
Learning
realtime vision
Segmentation
State of the art
structure learning
Training
Vegetation
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Summary: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. The result is an approach that obtains real time performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2013.231