Shape matching and registration by data-driven EM
Computer Vision and Image Understanding
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
Contour Extraction Using Particle Filters
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
International Journal of Computer Vision
Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning
International Journal of Computer Vision
Learning Fast Emulators of Binary Decision Processes
International Journal of Computer Vision
Evolution of a local boundary detector for natural images via genetic programming and texture cues
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Energy-Based Perceptual Segmentation Using an Irregular Pyramid
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Comparison of Perceptual Grouping Criteria within an Integrated Hierarchical Framework
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Lesion detection and segmentation in uterine cervix images using an ARC-level MRF
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Interactive rotoscoping: extracting and tracking object sketch
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detecting object boundaries using low-, mid-, and high-level information
Computer Vision and Image Understanding
Boundary detection using f-measure-, filter- and feature- (F3) boost
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Detecting faint curved edges in noisy images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Learning to detect roads in high-resolution aerial images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Turbopixel segmentation using Eigen-images
IEEE Transactions on Image Processing
Fast superpixels for video analysis
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Segmentation of crystalline lens in photorefraction video
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
An efficient color image segmentation algorithm using hybrid approaches
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Wave interference for pattern description
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Color texture image segmentation based on neutrosophic set and wavelet transformation
Computer Vision and Image Understanding
Normalized cut based edge detection
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Computational-geometry approach to digital image contour extraction
Transactions on computational science XIII
Gable roof description by self-avoiding polygon
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Learning class-specific edges for object detection and segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A segmentation quality measure based on rich descriptors and classification methods
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Object categorization with sketch representation and generalized samples
Pattern Recognition
Sketch-based image retrieval on mobile devices using compact hash bits
Proceedings of the 20th ACM international conference on Multimedia
A particle filter framework for contour detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Multi-frequency transformation for edge detection
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Automatic construction of invariant features using genetic programming for edge detection
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Genetic programming for automatic construction of variant features in edge detection
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Automatic construction of gaussian-based edge detectors using genetic programming
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Genetic programming for edge detection using multivariate density
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Computer Vision and Image Understanding
SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
International Journal of Computer Vision
TouchCut: Fast image and video segmentation using single-touch interaction
Computer Vision and Image Understanding
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Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.