Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Content-Based Image Retrieval Based on Local Affinely Invariant Regions
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Wide-baseline multiple-view correspondences
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Context information from search engines for document recognition
Pattern Recognition Letters
Detecting, tracking and recognizing license plates
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A method for text localization and recognition in real-world images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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A new class of image-level detectors that can be adapted by machine learning techniques to detect parts of objects from a given category is proposed. A classifier (e.g. neural network or adaboost trained classifier) within the detector selects a relevant subset of extremal regions, i.e. regions that are connected components of a thresholded image. Properties of extremal regions render the detector very robust to illumination change. Robustness to viewpoint change is achieved by using invariant descriptors and/or by modeling shape variations by the classifier. The approach is brought to bear on three problems: text detection, face segmentation and leopard skin detection. High detection rates were obtained for unconstrained (i.e. brightness, affine and font invariant) text detection (92%) with a reasonable false positive rate. The time-complexity of the detection is approximately linear in the number of pixels and a non-optimized implementation runs at about 1 frame per second for a 640× 480 image on a high-end PC.