摘要
计算机图像智能处理技术为服装设计师开展设计、启发灵感提供了方便和可能。通过提取布料图像的SURF特征可以实现布料图像形状分析,但由于SURF特征维数高、特征提取是基于灰度图进行,因此存在匹配速度慢、匹配结果不够符合人眼视觉特点的问题。本文提出了基于小波变换的自适应SURF特征提取算法和基于K-Means聚类的布料图像颜色分析方法。通过融合图像形状特征、颜色特征,加快了布料图像匹配速度,使布料图像的匹配结果更加符合人眼视觉感受。在8种不同类型布料图像上的实验验证了该算法的有效性。
Computer intel l igent image processing technology can provide an effective aid for dress designer. By extracting the SURF features,the image shape of the cloth can be recognized. However,due to the high feature dimension and the grayscale based feature extraction method of SURF, there exist shortcomings, e. g, slow image matching speed and the matching result is not enough to match the characteristics of human visual. Hence,this paper proposes an adaptive SURF feature extraction algorithm based on wavelet transform and an image color analysis method based on K-Means clustering. By fusing the shape and color feature of the image,the matching speed is accelerated and the matching results are made more accord with the human visual perception. Experiments via 8 different kinds of fabric images show the effectiveness of the proposed algorithm.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第1期105-112,共8页
Journal of East China University of Science and Technology
基金
国家自然科学基金(61371150)
