A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
An active vision architecture based on iconic representations
Artificial Intelligence - Special volume on computer vision
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Three-Dimensional Object Recognition Systems
Three-Dimensional Object Recognition Systems
Proceedings of the International Workshop on Object Representation in Computer Vision II
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Object Recognition Using Appearance-Based Parts and Relations
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On representation and matching of multi-coloured objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Journal of Cognitive Neuroscience
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Shape and color information are important cues for object recognition. An ideal system should give the option to use both forms of information, as well as the option to use just one of the two. We present in this paper a kernel method that achieves this goal. It is based on results of statistical physics ofd isordered systems combined with Gibbs distributions via kernel functions. Experimental results on a database of 100 objects confirm the effectiveness of the proposed approach.