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Abstract

Texture analysis plays an important role in many image processing applications to describe the image content or objects. On the other hand, visual surface defect detection is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this link a dual purpose benchmark dataset is proposed for texture image analysis and surface defect detection titled stone texture image (STI dataset). The proposed benchmark dataset consist of 4 different class of stone texture images. The proposed benchmark dataset have some unique properties to make it very near to real applications.  Dual purpose is the main advantage of the STI dataset  Local rotation in captured images  Different zoom rates in captured images  Local and global repetitive textures, because of natural features of stone kinds.  Low-size and high-size repetitive textures jointly  Different textures with same human visual scene  Low-size and high-size defects  Different kinds of abnormalities in color, texture and shape.

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