Feature extraction from faces using deformable templates
International Journal of Computer Vision
Neural Computation
Neural Computation
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bayesian frameworks for deformable pattern classification and retrieval: application to handwriting recognition
Independent Component Analysis Segmentation Algorithm
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
iCrux: an artificially intelligent virtual screen technology
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Deterministic length reduction: fast convolution in sparse data and applications
CPM'07 Proceedings of the 18th annual conference on Combinatorial Pattern Matching
Hi-index | 0.17 |
To achieve integrated segmentation and recognition in complex scenes, the model-based approach has widely been accepted as a promising paradigm. However, the performance is still far from satisfactory when the target object is highly deformed and the level of outlier contamination is high. In this paper, we first describe two Bayesian frameworks, one for classifying input patterns and another for detecting target patterns in complex scenes using deformable models. Then, we show that the two frameworks are similar to the forward-reverse setting of Hausdorff matching and that their matching and discriminating properties are complementary to each other. By properly combining the two frameworks, we propose a new matching scheme called bidirectional matching. This combined approach inherits the advantages of the two Bayesian frameworks. In particular, we have obtained encouraging empirical results on shape-based pattern extraction, using a subset of the CEDAR handwriting database containing handwritten words of highly varying shape.