CAJ | 학술논문

提出了一种新的简单有效的融合多颜色分量的分割方法，首先在六个不同的颜色空间中选择最佳的待分割颜色分量，然后应用直方图和空间模糊C均值（SFCM）技术对不同颜色分量进行自适应初始分割，最后融合分割结果并进行区域合并。利用该算法在Berkeley图像库上进行了大量实验，实验结果表明，与当前一些经典分割算法Mean-shift、FCR、CTM等相比，利用该方法能够获得更好的分割结果以及更优的性能指标。
제출료일충신적간단유효적융합다안색분량적분할방법，수선재륙개불동적안색공간중선택최가적대분할안색분량，연후응용직방도화공간모호C균치（SFCM）기술대불동안색분량진행자괄응초시분할，최후융합분할결과병진행구역합병。이용해산법재Berkeley도상고상진행료대량실험，실험결과표명，여당전일사경전분할산법Mean-shift、FCR、CTM등상비，이용해방법능구획득경호적분할결과이급경우적성능지표。
This paper proposes a new, simple, and efficient segmentation approach. It chooses the best segmentation com-ponents among six different color spaces, then Histogram and SFCM technique is applied for initialization of segmenta-tion. It fuses the segmentation results and merge regions. Extensive experiments have been taken on Berkeley image data-base by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, such as Mean-shift, FCR and CTM, etc, the method can achieve better image partitioning and better performance.