Geometry and photometry in three-dimensional visual recognition
Geometry and photometry in three-dimensional visual recognition
Face recognition: the problem of compensating for changes in illumination direction
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Generic Bilinear Calibration-Estimation Problem
International Journal of Computer Vision
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Antifaces: A Novel, Fast Method for Image Detection
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)
Shape and albedo from multiple images using integrability
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
Comparing Images under Variable Illumination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiplexing for Optimal Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper focuses on the detection of objects with a Lambertian surface under varying illumination and pose. We offer to apply a novel detection method that proceeds by modeling the different illuminations from a small number of images in a training set; this automatically voids the illumination effects, allowing fast illumination invariant detection, without having to create a large training set. It is demonstrated that the method "fits in" nicely with previous work about modeling the set of object appearances under varying illumination. In the experiments, an object was correctly detected under image plane rotations in a 45° range, and a wide variety of different illuminations, even when significant shadows were present.