摘要
针对提花毛皮样片扫描图像的花型自动识别问题,提出了一种改进的基于空间信息的模糊C均值聚类图像分割算法,该算法利用像点邻域区间的粗糙度和邻域像点的值修正像点与聚类中心的距离,对像点的模糊隶属度函数进行修正,利用修正后的模糊隶属度函数进行聚类中心的迭代计算,获得合理的聚类中心。经多幅提花毛皮样片的花型图像分割实验表明,该算法具有对噪声不敏感的优点,在进行提花毛皮样片扫描图像的花型识别时,能获得较好识别结果。
Aiming at the problem of pattern auto-recognition of jacquard fur sample scanning image, an improved fuzzy C-means clustering image segmentation algorithm based on spatial information is proposed, The distance between the pixel and the clustering center is modified by using the coarseness degree of the neighbor field and the value of the pixel, and the fuzzy membership function of the pixel is modified. The reasonable clustering center is gained by iterative calculation using modified fuzzy membership function. Mter several experiments of jacquard fur sample scanning image segmentation, it is indicated that the algorithm has the advantage of less sensitive to noise and it can get preferable recognition results when recognizing the pattern of jacquard fur sample scanning image.
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2007年第5期63-65,共3页
Journal of Textile Research
