Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Shape detection from line drawings with local neighborhood structure
Pattern Recognition
Contour based object detection using part bundles
Computer Vision and Image Understanding
Object recognition using junctions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Using partial edge contour matches for efficient object category localization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
From a set of shapes to object discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Voting by grouping dependent parts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Weakly supervised shape based object detection with particle filter
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning shape detector by quantizing curve segments with multiple distance metrics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Generic object class detection using boosted configurations of oriented edges
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Analyzing Ancient Maya Glyph Collections with Contextual Shape Descriptors
International Journal of Computer Vision
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Searching the past: an improved shape descriptor to retrieve maya hieroglyphs
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Contour-based object detection as dominant set computation
Pattern Recognition
Shape-Based Object Detection via Boundary Structure Segmentation
International Journal of Computer Vision
Drawing an automatic sketch of deformable objects using only a few images
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
We introduce a shape detection framework called Contour Context Selection for detecting objects in cluttered images using only one exemplar. Shape based detection is invariant to changes of object appearance, and can reason with geometrical abstraction of the object. Our approach uses salient contours as integral tokens for shape matching. We seek a maximal, holistic matching of shapes, which checks shape features from a large spatial extent, as well as long-range contextual relationships among object parts. This amounts to finding the correct figure/ground contour labeling, and optimal correspondences between control points on/around contours. This removes accidental alignments and does not hallucinate objects in background clutter, without negative training examples. We formulate this task as a set-to-set contour matching problem. Naive methods would require searching over 'exponentially' many figure/ground contour labelings. We simplify this task by encoding the shape descriptor algebraically in a linear form of contour figure/ground variables. This allows us to use the reliable optimization technique of Linear Programming. We demonstrate our approach on the challenging task of detecting bottles, swans and other objects in cluttered images.