Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Invariant Contour Completion Using Conditional Random Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Min-Cover Approach for Finding Salient Curves
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Multi-scale Improves Boundary Detection in Natural Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Contour Extraction Using Particle Filters
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement
IEEE Transactions on Image Processing
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We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which locally tracks small pieces of edges called edgelets. The combination of the Bayesian modeling and the edgelets enables the use of semi-local prior information and image-dependent likelihoods. We use a mixed offline and online learning strategy to detect the most relevant edgelets. The detection problem is then modeled as a sequential Bayesian tracking task, estimated using a particle filtering technique. Experiments on the Berkeley Segmentation Datasets show that the proposed Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods.