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
fDate :
6/1/2015 12:00:00 AM
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"
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2015.7299067