Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Face Recognition by Local Shape Difference Boosting
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper proposes a novel algorithm to efficiently compute the histograms in densely overlapped polygonal regions. An incremental scheme is used to reduce the computational complexity. By this scheme, only a few entries in an existing histogram need to be updated to obtain a new histogram. The updating procedure makes use of a few histograms attached to the polygon's edges, which can be efficiently pre-computed in a similar incremental manner. Thus, the overall process can achieve higher computational efficiency. Further, we extend our method to efficiently evaluate objective functions on the histograms in polygonal regions. The experiments on natural images demonstrate the high efficiency of our method.