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RGB Color Cluster and Graph Coloring Algorithm for Partial Color Blind Correction

机译:用于部分色盲校正的RGB颜色簇和图着色算法

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Partial color blindness is an anomaly occurring to 5-8% of the world's population. Color correction using re-coloring algorithm usually can be used to help partial color blindness patient. One of the existing re-coloring techniques is to use the RGB color cluster technique and combine it by utilizing the brute force algorithm to perform color tracing for correcting the color. It has massive time and memory complexity. This research aims to create a correction technique for color blind people using RGB Color Cluster combined with Graph Coloring Algorithm. The first process is to get the RGB color cluster for color blind subject. After getting RGB color cluster from the subject then the image which want to be corrected is grouped based on RGB color cluster. The threshold of color grouping in image is done by utilizing the upper and lower bound values of the RGB color cluster. After the cluster is grouped, then we can represent neighbourhood between the colors by utilizing graph. The adjacent color group will be a neighbour. The next process is color re-coloring using graph coloring algorithm. In graph coloring algorithm, same color group is prohibited to become neighbour. In this research, graph coloring algorithm is used to prevent 2 colors that are look almost similar for become neighbours because it will cause the subject cannot distinguish it. Re-coloring is done by increasing and decreasing the color intensity of a set of colors. This technique succeeds in decreasing the complexity of the brute force algorithm from O(N4) to O(2N2) where the first N2 is the complexity of building the cluster group and the second N2 is the complexity of the re- coloring. In addition, the color of the object becomes more natural because Re-coloring is based on color group not pixel based.
机译:色盲是世界人口的5-8%的异常现象。使用重着色算法的颜色校正通常可用于帮助部分色盲患者。现有的重新着色技术之一是使用RGB颜色簇技术,并通过使用蛮力算法将其组合以执行颜色跟踪以校正颜色。它具有大量的时间和内存复杂性。本研究旨在创建一种结合RGB颜色簇和图着色算法的色盲人群校正技术。第一个过程是获取色盲对象的RGB颜色簇。从被摄体获得RGB颜色聚类后,然后根据RGB颜色聚类对要校正的图像进行分组。通过利用RGB颜色簇的上限值和下限值来完成图像中颜色分组的阈值。将聚类分组后,我们可以利用图形表示颜色之间的邻域。相邻的颜色组将是邻居。下一步是使用图形着色算法对颜色进行重新着色。在图形着色算法中,禁止同一颜色组成为邻居。在这项研究中,使用图形着色算法来防止看起来几乎相似的2种颜色成为邻居,因为这会导致对象无法区分它。通过增加和减少一组颜色的颜色强度来完成重新着色。该技术成功地将蛮力算法的复杂度从O(N4)降低到O(2N2),其中第一个N2是构建群集组的复杂度,第二个N2是重新着色的复杂度。另外,由于重新着色基于颜色组而不是基于像素,因此对象的颜色变得更加自然。

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