Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
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
General Purpose Matching of Grey Level Arbitrary Images
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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In this paper we describe how kernel-based novelty detection can be used effectively to model 3D objects from unconstrained image sequences, in order to deal withob ject identification and recognition. In this framework, we introduce a similarity measure based on the Hausdorff distance, well suited to represent, identify, and recognize 3D objects from grey-level images. The effectiveness of the method is shown on the representation and identification of rigid 3D objects in cluttered environments.