The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
Multiclass boosting with repartitioning
ICML '06 Proceedings of the 23rd international conference on Machine learning
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Oriented filters for object recognition: an empirical study
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
On filtering by means of generalized integral images: a review and applications
Multidimensional Systems and Signal Processing
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We present a framework for object recognition based on simple scale and orientation invariant local features that when combined with a hierarchical multiclass boosting mechanism produce robust classifiers for a limited number of object classes in cluttered backgrounds. The system extracts the most relevant features from a set of training samples and builds a hierarchical structure of them. By focusing on those features common to all trained objects, and also searching for those features particular to a reduced number of classes, and eventually, to each object class. To allow for efficient rotation invariance, we propose the use of non-Gaussian steerable filters, together with an Orientation Integral Image for a speedy computation of local orientation.