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Published May 2011 | public
Journal Article

Contour Detection and Hierarchical Image Segmentation

Abstract

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by userspecified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

Additional Information

© 2011 IEEE. Manuscript received 16 Feb. 2010; accepted 5 July 2010; published online 19 Aug. 2010. Date of Current Version: 22 March 2011. Recommended for acceptance by P. Felzenszwalb.

Additional details

Created:
August 19, 2023
Modified:
October 23, 2023