DocumentCode
3672524
Title
DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
Author
Gedas Bertasius;Jianbo Shi;Lorenzo Torresani
Author_Institution
University of Pennsylvania, Philadelphia, 19104, United States
fYear
2015
fDate
6/1/2015 12:00:00 AM
Firstpage
4380
Lastpage
4389
Abstract
Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional approaches, we show that we can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, we exploit object-related features as high-level cues for contour detection.
Keywords
"Feature extraction","Computer architecture","Image edge detection","Convolutional codes","Object detection","Machine learning","Training"
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN
1063-6919
Type
conf
DOI
10.1109/CVPR.2015.7299067
Filename
7299067
Link To Document