Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Face recognition under varying illumination using gradientfaces
IEEE Transactions on Image Processing
Illumination normalization using logarithm transforms for face authentication
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Homomorphic filtering based illumination normalization method for face recognition
Pattern Recognition Letters
ORB: An efficient alternative to SIFT or SURF
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Feature matching is one of the basic approaches to many of computer vision applications, such as object recognition. Dealing with illumination variations is an open problem in this field. In this paper we present an approach to make a more robust algorithm against real world illumination changes and variations in direction of the light source on our object of interest, by using a set of training images for sampling these variations from their SIFT keypoints. A comprehensive keypoint descriptor based on the variations of illumination in training data is acquired to have a high recognition rate against real 3D illumination changes. This large number of keypoints is simplified to achieve a smaller number of robust keypoints and significantly faster matching phase.