The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
Generic surface interpretation: observability model
Proceedings of the 4th international symposium on Robotics Research
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Geometry and photometry in three-dimensional visual recognition
Geometry and photometry in three-dimensional visual recognition
A framework for the construction of reflectance maps for machine vision
CVGIP: Image Understanding
Face recognition: the problem of compensating for changes in illumination direction
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Object Representation form Lighting Variations
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
What Shadows Reveal about Object Structure
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Bidirectional Reflection Distribution Function Expressed in Terms of Surface Scattering Modes
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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)
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Recognition by Linear Combination of Models
Recognition by Linear Combination of Models
A deformable model for the recognition of human faces under arbitrary illumination
A deformable model for the recognition of human faces under arbitrary illumination
Journal of Cognitive Neuroscience
Hi-index | 0.00 |
Due to illumination variability, the same object can appear dramatically different even when viewed in fixed pose. Consequently, an object recognition system must employ a representation that is either invariant to, or models this variability. This chapter presents an appearance-based method for modeling this variability. In particular, we prove that the set of n-pixel monochrome images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in Rn and that the dimension of this illumination cone equals the number of distinct surface normals. For a non-convex object with a more general reflectance function, the set of images is also a convex cone. Geometric properties of these cones for monochrome and color cameras are considered. Here, present a method for constructing a cone representation from a small number of images when the surface is continuous, possibly non-convex, and Lambertian; this accounts for both attached and cast shadows. For a collection of objects, each object is represented by a cone, and recognition is performed through nearest neighbor classification by measuring the minimal distance of an image to each cone. We demonstrate the utility of this approach to the problem of face recognition (a class of non-convex and non-Lambertian objects with similar geometry). The method is tested on a database of 660 images of 10 faces, and the results exceed those of popular existing methods.