• 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