Lazy Contract Checking for Immutable Data Structures
Implementation and Application of Functional Languages
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
Contour based object detection using part bundles
Computer Vision and Image Understanding
From a set of shapes to object discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Computational-geometry approach to digital image contour extraction
Transactions on computational science XIII
Chaperones and impersonators: run-time support for reasonable interposition
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
A particle filter framework for contour detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Ortho-image analysis for producing lane-level highway maps
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
International Journal of Computer Vision
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We consider the problem of deriving a global interpretation of an image in terms of a small set of smooth curves. The problem is posed using a statistical model for images with multiple curves. Besides having important applications to edge detection and grouping the curve finding task is a special case of a more general problem, where we want to explain the whole image in terms of a small set of objects. We describe a novel approach for estimating the content of scenes with multiple objects using a min-cover framework that is simple and powerful. The min-cover problem is NP-hard but there is a good approximation algorithm that sequentially selects objects minimizing a "cost per pixel" measure. In the case of curve detection we use a type of best-first search to quickly find good curves for the covering algorithm. The method integrates image data over long curves without relying on binary feature detection. We have applied the curve detection method for finding object boundaries in natural scenes and measured its performance using the Berkeley segmentation dataset.