Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
General Purpose Matching of Grey Level Arbitrary Images
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
A Cluster-Based Statistical Model for Object Detection
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes
International Journal of Computer Vision
Binary-image comparison with local-dissimilarity quantification
Pattern Recognition
Robust matching and recognition using context-dependent kernels
Proceedings of the 25th international conference on Machine learning
Classifying materials in the real world
Image and Vision Computing
Cue integration through discriminative accumulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Learning one class at a time can be seen as an effective solution to classification problems in which only the positive examples are easily identifiable. A kernel method to accomplish this goal consists of a representation stage - which computes the smallest sphere in feature space enclosingthe positive examples - and a classification stage - which uses the obtained sphere as a decision surface to determine the positivity of new examples. In this paper we describe a kernel well suited to represent, identify, and recognize 3D objects from unconstrained images. The kernel we introduce, based on Hausdorff distance, is tailored to deal with grey-level image matching. The effectiveness of the proposed method is demonstrated on several data sets of faces and objects of artistic relevance, like statues.