TY - GEN
T1 - Contour detection via random forest
AU - Zhang, Chao
AU - Ruan, Xiang
AU - Zhao, Yuming
AU - Yang, Ming Hsuan
PY - 2012
Y1 - 2012
N2 - Contour detection is an important and fundamental problem in computer vision that finds numerous applications. In this paper, we propose a learning algorithm for contour detection via random forest. Visual cues that can be extracted easily and efficiently are integrated to learn a detector where the decision of an contour pixel is made independently via the random forest at each location in the image. We evaluate the proposed algorithm against leading methods in the literature on the Berkeley Segmentation Dataset. Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy.
AB - Contour detection is an important and fundamental problem in computer vision that finds numerous applications. In this paper, we propose a learning algorithm for contour detection via random forest. Visual cues that can be extracted easily and efficiently are integrated to learn a detector where the decision of an contour pixel is made independently via the random forest at each location in the image. We evaluate the proposed algorithm against leading methods in the literature on the Berkeley Segmentation Dataset. Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84874563139&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84874563139
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2772
EP - 2775
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -