Shock Graphs and Shape Matching
International Journal of Computer Vision
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Browsing graphics without prior knowledge
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ALSBIR: A local-structure-based image retrieval
Pattern Recognition
An interactive example-driven approach to graphics recognition in engineering drawings
International Journal on Document Analysis and Recognition
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Path Similarity Skeleton Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry-Based Image Retrieval in Binary Image Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Morphology based spatial relationships between local primitives in line drawings
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Journal of Visual Communication and Image Representation
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An object detection method from line drawings is presented. The method adopts the local neighborhood structure as the elementary descriptor, which is formed by grouping several nearest neighbor lines/curves around one reference. With this representation, both the appearance and the geometric structure of the line drawing are well described. The detection algorithm is a hypothesis-test scheme. The top k most similar local structures in the drawing are firstly obtained for each local structure of the model, and the transformation parameters are estimated for each of the k candidates, such as object center, scale and rotation factors. By treating each estimation result as a point in the parameter space, a dense region around the ground truth is then formed provided that there exist a model in the drawing. The mean shift method is used to detect the dense regions, and the significant modes are accepted as the occurrence of object instances.