Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Moment Forms: Their Construction and Application to Object Identification and Positioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric invariants and object recognition
International Journal of Computer Vision
Nonorthogonal image expansion related to optimal template matching in complex images
CVGIP: Graphical Models and Image Processing
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Analysis of Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Journal of Cognitive Neuroscience
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This paper offers a novel detection method, which works well even in the case of a complicated image collection - for instance, a frontal face under a large class of linear transformations. It was also successfully applied to detect 3D objects under different views. Call the class of images, which should be detected, a multi-template. The detection problem is solved by sequentially applying very simple filters (or detectors), which are designed to yield small results on the multi-template (hence "anti-faces"), and large results on "random" natural images. This is achieved by making use of a simple probabilistic assumption on the distribution of natural images, which is borne out well in practice, and by using a simple implicit representation of the multi-template. Only images which passed the threshold test imposed by the first detector are examined by the second detector, etc. The detectors have the added bonus that they act independently, so that their false alarms are uncorrelated; this results in a percentage of false alarms which exponentially decreases in the number of detectors. This, in turn, leads to a very fast detection algorithm, usually requiring (1 + δ)N operations to classify an N-pixel image, where δ The suggested algorithm's performance favorably compares to the well-known eigenface and support vector machine based algorithms, and it is substantially faster.