Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Object Detection and Localization by Dynamic Template Warping
International Journal of Computer Vision
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Object Detection and Localization by Dynamic Template Warping
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Model for a gradual objects recognition
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Object recognition using point uncertainty regions as pose uncertainty regions
Image and Vision Computing
| Hi-index | 0.00 |
This work describes a statistical approach to deal with learning and recognition problems in the field of computer vision. An abstract theoretical framework is provided, which is suitable for automatic model generation from examples, identification, and localization of objects. Both, the learning and localization stage are formalized as parameter estimation tasks. The statistical learning phase is unsupervised with respect to the matching of model and scene features. The general mathematical description yields algorithms which can even treat parameter estimation problems from projected data. The experiments show that this probabilistic approach is suitable for solving 2D and 3D object recognition problems using grey-level images. The method can also be applied to 3D image processing issues using range images, i.e. 3D input data.