Object Detection and Localization by Dynamic Template Warping
International Journal of Computer Vision
Recognition, Resolution, and Complexity of Objects Subject to Affine Transformations
International Journal of Computer Vision
Robust Parameterized Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Recognition for Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Object Detection and Localization by Dynamic Template Warping
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Active blobs: region-based, deformable appearance models
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Fast normalized cross correlation for defect detection
Pattern Recognition Letters
Robust parameterized component analysis: theory and applications to 2D facial appearance models
Computer Vision and Image Understanding - Special issue on Face recognition
An integrated dynamic scene algorithm for segmentation and motion estimation
EURASIP Journal on Applied Signal Processing
Comparison of Optimisation Algorithms for Deformable Template Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Proceedings of the 32nd DAGM conference on Pattern recognition
A new method for traffic signs classification using probabilistic neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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A fast simulated annealing algorithm is developed for automatic object recognition. The object recognition problem is addressed as the problem of best describing a match between a hypothesized object and an image. The normalized correlation coefficient is used as a measure of the match. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, e.g., traffic signs, can be recognized by a navigating robot. We illustrate the performance of our algorithm with real-world images of complicated scenes with traffic signs. False positive matches occur only for templates with very small information content. To avoid false positive matches, we propose a method to select model images for robust object recognition by measuring the information content of the model images. The algorithm works well in noisy images for model images with high information content.