Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing optical flow across multiple scales: an adaptive coarse-to-fine strategy
International Journal of Computer Vision
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
From coarse to fine skin and face detection
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Antifaces: A Novel, Fast Method for Image Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Mustererkennung 1998, 20. DAGM-Symposium
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Segmentation and Tracking of Faces in Color Images
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
View-Based Active Appearance Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Learning and example selection for object and pattern detection
Learning and example selection for object and pattern detection
Neural network-based face detection
Neural network-based face detection
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coarse-to-Fine Strategy for Multiclass Shape Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Methodology for Class Detection Problems with Skewed Priors
Journal of Classification
Linear Asymmetric Classifier for cascade detectors
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Design Principle for Coarse-to-Fine Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning a hierarchy of classifiers for multi-class shape detection
Learning a hierarchy of classifiers for multi-class shape detection
Novel Design of Decision-Tree-Based Support Vector Machines Multi-class Classifier
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Greedy-based design of sparse two-stage SVMs for fast classification
Proceedings of the 29th DAGM conference on Pattern recognition
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection
Engineering Applications of Artificial Intelligence
Computers in Biology and Medicine
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We introduce a computational design for pattern detection based on a tree-structured network of support vector machines (SVMs). An SVM is associated with each cell in a recursive partitioning of the space of patterns (hypotheses) into increasingly finer subsets. The hierarchy is traversed coarse-to-fine and each chain of positive responses from the root to a leaf constitutes a detection. Our objective is to design and build a network which balances overall error and computation. Initially, SVMs are constructed for each cell with no constraints. This "free network" is then perturbed, cell by cell, into another network, which is "graded" in two ways: first, the number of support vectors of each SVM is reduced (by clustering) in order to adjust to a pre-determined, increasing function of cell depth; second, the decision boundaries are shifted to preserve all positive responses from the original set of training data. The limits on the numbers of clusters (virtual support vectors) result from minimizing the mean computational cost of collecting all detections subject to a bound on the expected number of false positives. When applied to detecting faces in cluttered scenes, the patterns correspond to poses and the free network is already faster and more accurate than applying a single pose-specific SVM many times. The graded network promotes very rapid processing of background regions while maintaining the discriminatory power of the free network.