{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:17:13Z","timestamp":1760239033034,"version":"build-2065373602"},"reference-count":100,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder\u2013decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&amp;E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.<\/jats:p>","DOI":"10.3390\/s20185432","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T09:40:56Z","timestamp":1600767656000},"page":"5432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1318-4377","authenticated-orcid":false,"given":"Lucas de Assis","family":"Soares","sequence":"first","affiliation":[{"name":"Federal Institute of Esp\u00edrito Santo, Linhares 29901-291, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7793-0693","authenticated-orcid":false,"given":"Klaus Fabian","family":"C\u00f4co","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Esp\u00edrito Santo, Vit\u00f3ria 29075-910, Brazil"}]},{"given":"Patrick Marques","family":"Ciarelli","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Esp\u00edrito Santo, Vit\u00f3ria 29075-910, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8287-3045","authenticated-orcid":false,"given":"Evandro Ottoni Teatini","family":"Salles","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Esp\u00edrito Santo, Vit\u00f3ria 29075-910, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s11263-018-1125-z","article-title":"From BoW to CNN: Two decades of texture representation for texture classification","volume":"127","author":"Liu","year":"2019","journal-title":"Int. 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