Computer Vision, Graphics, and Image Processing
Computer analysis of regular repetitive textures
Proceedings of a workshop on Image understanding workshop
Extracting periodicity of a regular texture based on autocorrelation functions
Pattern Recognition Letters
Modeling image textures by Gibbs random fields
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Modeling Visual Patterns by Integrating Descriptive and Generative Methods
International Journal of Computer Vision
Detecting, localizing and grouping repeated scene elements from an image
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Noncombinatorial Detection of Regular Repetitions under Perspective Skew
IEEE Transactions on Pattern Analysis and Machine Intelligence
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Near-regular texture analysis and manipulation
ACM SIGGRAPH 2004 Papers
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Texture periodicity detection: features, properties, and comparisons
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We present a method for extracting a lattice from near-regular texture. Our method demands minimal user intervention, needing a single mouse click to select a typical texton. The algorithm follows a four-step approach. First, an estimate of texton size is obtained by considering the spacing of peaks in the auto-correlation of the texture. Second, a sample of the image around the user-selected texton is correlated with the image. Third, the resulting correlation surface is converted to a map of potential texton centres using non-maximal suppression. Finally, the maxima are formed into a graph by connecting potential texton centres. We have found the method robust in the face of significant changes in pixel intensity and geometric structure between textons.