摘要
为了提高纹理图像分割的边缘准确性和区域一致性,提出了一种利用同步自回归模型和小波特征实现纹理图像分割的方法,包括特征提取、粗分割和细分割3个阶段.先提取图像的同步自回归模型参数特征,然后利用K均值聚类实现对纹理图像的粗分割,细分割则是在粗分割的基础上提取图像的小波特征,然后利用最小距离分类器对粗分割图像中不稳定象素进行重新分类,实现图像的最后分割.
To improve the accuracy of boundary locations and region homogeneity,a approach based on simultaneous autoregressive model and wavelet-transform is proposed in this paper. This technique contains three stages： feature extraction, pre-segmentation and post-segmentation. Firstly, texture features are extracted by using the simultaneous autoregressive model, then the original image is segmented initially using the k-means clustering algorithm on the pre-segmentation stage. The post-segmentation is performed based on the result of pre-segmentation,in order to extract original image texture feature based on wavelet-transform. then the minimum distance classifier is used to classify the uncertain pixels,so the segmented image is achieved. Compared with traditional method, the present approach shows visible improvements both in diminishing segmentation error and in increasing boundary precision and region harmony.
出处
《河北师范大学学报（自然科学版）》
CAS
北大核心
2005年第5期477-480,共4页
Journal of Hebei Normal University：Natural Science
基金
河北省科学技术攻关项目(05212110d)
关键词
同步自回归模型
小波变换
聚类
纹理分割
simultaneous autoregressive model
wavelet transform
clustering
texture segmentation
