| 基于区域划分的多特征纹理图像分割 |
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| 引用本文： | 赵泉华,高郡,李玉.基于区域划分的多特征纹理图像分割[J].仪器仪表学报,2015,36(11):2519-2530. |
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| 作者姓名： | 赵泉华 高郡 李玉 |
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| 作者单位： | 辽宁工程技术大学 测绘与地理科学学院遥感科学与应用研究所辽宁123000 |
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| 基金项目： | 国家自然科学基金(41301479)、辽宁省自然科学基金(2015020090)项目资助 |
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| 摘 要： | 由于纹理图像的复杂性和多样性,仅依靠传统的单一特征实现纹理图像分割无法满足其对分割精度的要求。本文提出结合区域划分的多特征纹理图像分割方法。首先,依据像素灰度的空间相关性定义多个纹理特征;然后利用区域划分将图像域划分成不同子区域,待分割同质区域由这些子区域拟合而成;通过分别定义多个特征图像的同质区域之间的异质性势能函数和刻画各子区域邻域关系势能函数来定义全局势能函数,并构建非约束吉布斯概率分布,从而建立纹理分割模型;最后,采用M-H算法采样上述概率分布,从而获得最优图像分割结果。分别对模拟纹理图像、遥感图像、自然纹理图像和SAR海冰图像进行了分割实验,并与利用单一特征得到的分割结果进行对比分析,定性和定量的测试结果验证了算法的有效性。
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| 关 键 词： | 区域划分 多特征 纹理图像 图像分割 |
Multi feature texture image segmentation based on tessellation technique |
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| Zhao Quanhu,Gao Jun,Li Yu.Multi feature texture image segmentation based on tessellation technique[J].Chinese Journal of Scientific Instrument,2015,36(11):2519-2530. |
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| Authors: | Zhao Quanhu Gao Jun Li Yu |
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| Affiliation: | Institute for Remote Sensing and Application, School of Geomatics, Liaoning Technical University, Liaoning 123000, China |
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| Abstract: | Due to the complexity and diversity of texture image, traditional single feature based texture image segmentation method can not meet the requirement of segmentation accuracy. In order to improve the segmentation accuracy, this paper proposes a multiple features based texture image segmentation method that combines tessellation. Firstly, multiple texture features are defined according to the spatial correlation of the pixel greyscale. Then, the image region is divided into sub regions with tessellation, and the homogeneous regions to be segmented are obtained from fitting these sub regions. Furthermore, the global potential energy function is defined through defining the heterogenous potential energy function among the homogeneous regions of multiple feature images and the potential energy function describing the neighborhood relationship of the sub regions. The non constrained Gibbs probability distribution is built to construct a posterior distribution from which a texture segmentation model is established. Finally, the M H (metropolis hastings) algorithm is used to sample the posterior probability distribution, and the optimal image segmentation result is achieved based on the maximum a posterior (MAP) estimation. The segmentation experiments on the simulated texture images, remote sensing images, natural texture images as well as SAR (synthetic aperture radar, SAR) sea ice images were conducted, and the segmentation results were analyzed and compared with those obtained using the single feature method; the qualitative and quantitative test results verify the effectiveness of the proposed algorithm. |
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| Keywords: | tessellation multiple features texture image image segmentation |
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