02012nas a2200241   4500000000100000000000100001008004100002260001200043653002100055653001100076653002400087653001000111100001900121700001800140700001800158700002200176245008200198856009800280300001000378490000600388520136200394022001401756       2013                            d  c06/201310aImage Processing10aKmeans10aDefect Segmentation10aFruit1 aShiv Ram Dubey1 aPushkar Dixit1 aNishant Singh1 aJay Prakash Gupta00aInfected Fruit Part Detection using K-Means Clustering Segmentation Technique  uhttp://www.ijimai.org/journal/sites/default/files/files/2013/06/ijimai20132_2_9_pdf_10021.pdf  a65-720 v23 aNowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising.  a1989-1660