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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Universal Semantic-less Texture Boundary Detection for Microscopy (and Metallography)

Matan Rusanovsky · Ofer Beeri · Shai Avidan · Gal Oren


The automated analysis of textures has always been a topic of importance in metallographic imaging in particular and in microscopy in general. Those analyzed textures are used in a variety of applications, and as such, texture analysis is at the backbone of most, if not all, other vision tasks. However, the task of texture analysis greatly differs from related and well-defined tasks such as edge, contour, and semantic analysis for detection and segmentation. As texture perception is hard even for humans to define and includes a subjective outlook, computerized texture-based segmentation in semantic-less and texture-oriented images has not been achieved so far. Moreover, it is difficult to apply recent computer vision algorithms to such images because of database shortages in this domain, as well as a shortage of accurately labeled data.Therefore, we wish to develop a Universal Texture Representation (UTR). This representation will allow us to segment any texture image in any domain and even develop a Universal Texture Boundary Detector (UTBD). Crucially, such algorithms should work on texture images (and even videos) that have no semantic meaning, such as metallographic textures; hence, the vast literature on edge, contour, and semantic segmentation can not be used as is in our context. Henceforth, we formulate and define our problem: Universal semantic-less texture boundary detection. A solution to this newly defined problem - which, in this work, we present the initial path towards on our Texture Boundary in Metallography (TBM) dataset - could be used in a variety of applications as is or as an enhancer to other closely related vision tasks. For example, it could help quickly segment new images based on single-click segmentation cues provided by the user, or it could help retrieve images with similar textures from past experiments.

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