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Extracting periodicity of a regular texture based on autocorrelation functions
Pattern Recognition Letters
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ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
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Higher level segmentation: Detecting and grouping of invariant repetitive patterns
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Algorithms for detection of repeating patterns in images often assume that the repetition is regular and highly similar across the instances. Approximate repetitions are also of interest in many domains such as hand carved sculptures, wall decorations, groups of natural objects, etc. Detection of such repetitive structures can help in applications such as image retrieval, image inpainting, 3D reconstruction, etc. In this work, we look at a specific class of approximate repetitions: those in images of hand carved relief structures. We present a robust hierarchical method for detecting such repetitions. Given a single image with reliefs, our algorithm finds dense matches of local features across the image at various scales. The matching features are then grouped based on their geometric configuration to find repeating elements. We also propose a method to group the repeating elements to segment the repetitive patterns in an image. In relief images, foreground and background have nearly the same texture, and matching of a single feature would not provide reliable evidence of repetition. Our grouping algorithm integrates evidences of repetition to reliably find repeating patterns. Input image is processed on a scale-space pyramid to effectively detect all possible repetitions at different scales. Our method has been tested on images with large varieties of complex repetitive patterns and the qualitative results show the robustness of our approach.