Many image acquisition techniques used in biomedical imaging, material analysis, or structural geology are capable to acquire 3\u2013D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3\u2013D content. One of the most common methods to analyze 3\u2013D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3\u2013D. Current state\u2013of\u2013the\u2013art techniques face many challenges when working with 3\u2013D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3\u2013D Riesz\u2013wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and to compare textures with arbitrary (local) orientations. This paper compares three local alignment criteria for higher\u2013order 3\u2013D Riesz\u2013wavelet transforms. The estimations of local texture orientations are based on higher\u2013order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3\u2013D solid textures with alterations (e.g., rotations, noise) demonstrated the importance of local directional information for robust and<\/div>\n

accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors by up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which<\/div>\n

are better than the other techniques tested on the same database.<\/div>\n

Abstract<\/strong><\/p>\n

<\/body><\/html><\/p>\n","protected":false},"excerpt":{"rendered":"

The paper ‘3-D Solid Texture Classification Using Locally-Oriented Wavelet Transforms’ by\u00a0Dicente et al.\u00a0has been accepted for publication at IEEE Transactions on Image Processing. Abstract Many image acquisition techniques used in biomedical imaging, material analysis, or structural geology are capable to acquire 3\u2013D solid images. Computational analysis of these images is complex but necessary, since it … <\/p>\n