Texture Segmentation by Evidence Gathering

A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumulator is used to store votes for each texture class. Tests on the Brodatz database and Berkeley Segmentation Dataset have shown that our algorithm provides excellent results; an average of 86.9% was achieved over 50 tests on 27 Brodatz textures compared with 80.3% achieved by segmentation by histogram comparison centred on each pixel. In addition, our results provide noticeably smoother texture boundaries and reduced noise within texture regions. The concept is also a "higher order" texture descriptor, whereby the arrangement of texture elements is used for classification as well as the frequency of occurrence that is featured in standard texture operators. This results in a unique descriptor for each texture class based on the structure of texture elements within the image, which leads to a homogeneous segmentation, in boundary and area, of texture by this new technique

Multi texture analysis of colorectal cancer continuum using multispectral imagery

Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images

Superconducting String Texture

We present a detailed analytical and numerical study of a novel type of static, superconducting, classically stable string texture in a renormalizable topologically trivial massive U(1) gauge model with one charged and one neutral scalar. An upper bound on the mass of the charged scalar as well as on the current that the string can carry are established. A preliminary unsuccesful search for stable solutions corresponding to large superconducting loops is also reported.Comment: RevTex, 14 pages, 8 figure

Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected in the wild.The resulting Describable Textures Dataset (DTD) is the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio

Texture measurement on gutted cod during storage in ice using a hand-held instrument

Fish muscle as food is to be seen as highly perishable. In unfrozen fish, freshness is considered the most important quality attribute. It is well known that there are several biochemical changes that can affect dramatically the texture of fish muscle. Immediately after death the fish texture is soft and elastic. In connection with rigor mortis the fish texture changes markedly. It becomes harder during rigor and after its resolution it becomes softer. This softness increases due to proteolysis during further storage at refrigerated conditions. Texture is a very important indicator for evaluating the quality of fish. Barroso et al. (1997) have recently reviewed mechanical methods in use for texture measurements on fresh fish. Further reviews on texture measurement performed on fish muscle were recently published underlining the importance of texture as quality attribute (Hyldig et al 2001, Coppes et al. 2002). The position along the fish can influence the results and was investigated by several authors (Sigurgis-ladottir et. al. 1999). Different methods have been compared for their ability to differentiate between recently killed salmon and salmon stored on ice for up to 24 days (Veland et al. 1999)