A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Recovering Shading from Color Images
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recovering Intrinsic Images from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination normalization with time-dependent intrinsic images for video surveillance
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Efficient object segmentation using digital matting for MPEG video sequences
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
Automatic change detection of driving environments in a vision-based driver assistance system
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In real world, a scene is composed by many characteristics Intrinsic images represent these characteristics by two components, reflectance (the albedo of each point) and shading (the illumination of each point) Because reflectance images are invariant under different illumination conditions, they are more appropriate for some vision applications, such as recognition, detection We develop the system to separate them from a single image Firstly, a presented method, called Weighted-Map Method, is used to separate reflectance and shading A weighted map is created by first transforming original color domain into new color domain and then extracting some useful property Secondly, we build Markov Random Fields and use Belief Propagation to propagate local information in order to help us correct misclassifications from neighbors According to our experimental results, our system can apply to not only real images but also synthesized images.