Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient iterative algorithm for image thresholding
Pattern Recognition Letters
Texture analysis and classification using deterministic tourist walk
Pattern Recognition
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
An adaptable threshold detector
Information Sciences: an International Journal
Lacunarity as a texture measure for address block segmentation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A modified valley-emphasis method for automatic thresholding
Pattern Recognition Letters
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A new approach is proposed based on gliding-box method to estimate the lacunaritys of texture images. The first approach uses a local threshold to enhance local features of the texture pattern, so that, lacunarity is computed in terms of the local binary patterns that exist in the image, a characteristic which is proven to be a very powerful texture feature. The second approach simply expands the concept of gliding a box over the gray level intensity axis of the texture. As this task is a very time consuming one, we present an algorithm to speed up this process. The results have been tested in a natural texture database, a real and complex problem because the high variability intra-class and great similarity between classes, and compared with traditional lacunarity approaches to show that this new method is computationally simple, convenient and accurate.