Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-stage Contour Based Detection of Deformable Objects
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using partial edge contour matches for efficient object category localization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Weakly Supervised Learning of Interactions between Humans and Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
We propose a method to automatically extract a sketch of a common object structure present in a small set of real world weakly-labeled images. Applying a part-based deformable contour matching technique gives the location of repeatable contours. An initial deformable search strategy selects a set of salient, repeatable contours robust to a large range of non-rigid deformations. A contour completion technique based on a locally greedy bi-directional search strategy is adopted to merge the repeatable contour fragments for obtaining a complete shape. The output of our algorithm is used as an input to a sketch-based object-recognizer with results that are either better, or on par with those obtained with the ground truth sketches provided with the dataset.