The Selection of Natural Scales in 2D Images Using Adaptive Gabor Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Window-size determination for granulometrical structural texture classification
Pattern Recognition Letters
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Probabilistic Estimation of Local Scale
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Near-regular texture analysis and manipulation
ACM SIGGRAPH 2004 Papers
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual saliency by keypoints distribution analysis
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Discovering texture regularity as a higher-order correspondence problem
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture ''scale'' as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A ''mode'' vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging.