Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering perspective pose with a dual step EM algorithm
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Canonical Frames for Planar Object Recognition
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Cartographic Indexing into a Database of Remotely Sensed Images
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Sensitivity Analysis for Structural Matching
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Scene analysis using appearance-based models and relational indexing
ISCV '95 Proceedings of the International Symposium on Computer Vision
Relational Histograms for Shape Indexing
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Object Recognition Using Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Based Object Recognition Using Shape-from-Shading
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Graph-Based Methods for Vision: A Yorkist Manifesto
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Structural Sensivity for Large-Scale Line-Pattern Recognition
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
A Graph-Theoretic Approach to Image Database Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
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This paper presents a new similarity measure for object recognition from large libraries of line-patterns. The measure draws its inspiration from both the Hausdorff distance and a recently reported Bayesian consistency measure that has been sucessfully used for graphbased correspondence matching. The measure uses robust error-kernels to gauge the similarity of pairwise attribute relations defined on the edges of nearest neighbour graphs. We use the similarity measure in a recognition experiment which involves a library of over 1000 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 98%. A comparative study reveals that the method is most effective when a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms Rucklidge's median Hausdorff distance.