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- W2971067806 abstract "We propose a new direct approach for material recognition under diverse illumination and viewing conditions based on visual texture. We apply K-means clustering to feature vectors that consist of steerable filter subband statistics and dominant colors of each texture image in order to obtain a small number of exemplars characterizing each material. We then use structural texture similarity metrics and color composition metrics to compare a query texture to the exemplars for material classification. Experimental results using the CUReT database establish the importance of color and demonstrate that five exemplars per texture provide performance comparable to the state of the art." @default.
- W2971067806 created "2019-09-05" @default.
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- W2971067806 date "2019-09-01" @default.
- W2971067806 modified "2023-10-17" @default.
- W2971067806 title "Structural Texture Similarity for Material Recognition" @default.
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- W2971067806 doi "https://doi.org/10.1109/icip.2019.8803648" @default.
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