Application noteColor texture segmentation for clothing in a computer-aided fashion design system
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Cited by (33)
Fundamentals of common computer vision techniques for fashion textile modeling, recognition, and retrieval
2018, Applications of Computer Vision in Fashion and TextilesNovel initialization scheme for Fuzzy C-Means algorithm on color image segmentation
2013, Applied Soft Computing JournalCitation Excerpt :In general, there are two popular variants of HSI color space, namely the HSL and HSV color spaces. The HSI color space has been used extensively for image processing [40–42] due to their intuitive appeal to human's perception and their provision for isolating the luminance component [41]. Thorough experimental studies have been presented in [39] to measure the impact of the use of different color spaces on the performance of color texture analysis methods such as segmentation or classification.
An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery
2005, Pattern Recognition LettersColor image segmentation based on three levels of texture statistical evaluation
2005, Applied Mathematics and ComputationLearning to track colored objects with log-polar vision
2004, MechatronicsA novel initialization scheme for the fuzzy c-means algorithm for color clustering
2004, Pattern Recognition LettersCitation Excerpt :The CIELAB color space, adopted as an international standard in the 1970’s, provides perceptually uniform space, which means the Euclidean distance between two color points in the CIELAB color space corresponds to the perceptual difference between the two colors by the human vision system (Wyszecki and Stiles, 2000). This property have made the CIELAB color space be attractive and useful for color analysis, and the CIELAB color space has shown its superior performance than other color spaces in many color image application (Paschos, 2001; Gong et al., 1998; Chang and Wang, 1996; Li and Yuen, 2000; Shafarenko et al., 1998). Based on these reports, we chose the CIELAB color space for color clustering, and the color difference formula in the CIELAB color space played an important role in computing the dissimilarities between colors and developing the color membership function.