Multiscale Nonlinear Decomposition: The Sieve Decomposition Theorem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Connected Rotation-Invariant Size-Shape Granulometries
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Multiscale recursive medians, scale-space, and transforms with applications to image processing
IEEE Transactions on Image Processing
Correspondence regions and structured images
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Computer assisted diagnosis of microcalcifications in mammograms: a scale-space approach
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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We introduce three new texture features that are based on the morphological scale-space operator known as the sieve. The new features are tested on two databases. The first, the Outex texture database, contains Brodatz-like textures captured under constant illumination, scale and rotation. The second, the Outex natural scene database, contains images of real-world scenes taken under variable conditions. The new features are compared to univariate granulometries, with which they have some similarities, and to the dual-tree complex wavelet transform, local binary patterns and co-occurrence matrices. The features based upon the sieve are shown to have the best overall performance.