22 June 2015 Identification and sorting of regular textures according to their similarity
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Regardless whether mosaics, material surfaces or skin surfaces are inspected their texture plays an important role. Texture is a property which is hard to describe using words but it can easily be described in pictures. Furthermore, a huge amount of digital images containing a visual description of textures already exists. However, this information becomes useless if there are no appropriate methods to browse the data. In addition, depending on the given task some properties like scale, rotation or intensity invariance are desired. In this paper we propose to analyze texture images according to their characteristic pattern. First a classification approach is proposed to separate regular from non-regular textures. The second stage will focus on regular textures suggesting a method to sort them according to their similarity. Different features will be extracted from the texture in order to describe its scale, orientation, texel and the texel’s relative position. Depending on the desired invariance of the visual characteristics (like the texture’s scale or the texel’s form invariance) the comparison of the features between images will be weighted and combined to define the degree of similarity between them. Tuning the weighting parameters allows this search algorithm to be easily adapted to the requirements of the desired task. Not only the total invariance of desired parameters can be adjusted, the weighting of the parameters may also be modified to adapt to an application-specific type of similarity. This search method has been evaluated using different textures and similarity criteria achieving very promising results.
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Pilar Hernández Mesa, Pilar Hernández Mesa, Johannes Anastasiadis, Johannes Anastasiadis, Fernando Puente León, Fernando Puente León, } "Identification and sorting of regular textures according to their similarity", Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300A (22 June 2015); doi: 10.1117/12.2184439; https://doi.org/10.1117/12.2184439

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