针对常用的点对MRF(Markov random field)分割算法采用简单的先验模型,在对乳腺钼靶X图像中的乳腺肿块分割时产生的过分割问题,提出一种基于SLIC(simple linear iterative cluster)算法改进的MRF分割算法。采用SLIC算法将图像预分割为...针对常用的点对MRF(Markov random field)分割算法采用简单的先验模型,在对乳腺钼靶X图像中的乳腺肿块分割时产生的过分割问题,提出一种基于SLIC(simple linear iterative cluster)算法改进的MRF分割算法。采用SLIC算法将图像预分割为内部一致性较高的超像素区域,根据超像素区域的特征建立邻域系统并构建MRF,以超像素区域代替像素点作为分割单位实现乳腺肿块分割。实验结果表明,采用该算法对乳腺肿块进行分割可以高效获得较为准确的分割效果。展开更多
为提高水体周边环境的变化检测结果的精度,提出一种改进的变化检测方法。在光谱与纹理特征结合的基础上融合指数特征构建混合特征空间,采用超像素生成算法(simple linear iterative cluster,SLIC)处理叠加影像获取地物对象,并综合地物...为提高水体周边环境的变化检测结果的精度,提出一种改进的变化检测方法。在光谱与纹理特征结合的基础上融合指数特征构建混合特征空间,采用超像素生成算法(simple linear iterative cluster,SLIC)处理叠加影像获取地物对象,并综合地物对象的正反向异质信息构建地物对象的正反向异质性;使用最大数学期望算法与贝叶斯最小错误率理论获取两时相的变化信息,排除植被伪变化信息,形成相对准确和鲁棒的检测结果。试验结果表明:该方法能够有效区分水体周边环境中感兴趣的地物变化信息与不感兴趣的干扰信息、"伪变化信息"等,虚检率和漏检率较低,且正确率较高为96%以上,能够智能发现湖库水域周边"非正常"土地利用变化。展开更多
Simple linear iterative cluster(SLIC) is widely used because controllable superpixel number, accurate edge covering, symmetrical production and fast speed of calculation. The main problem of the SLIC algorithm is its ...Simple linear iterative cluster(SLIC) is widely used because controllable superpixel number, accurate edge covering, symmetrical production and fast speed of calculation. The main problem of the SLIC algorithm is its under-segmentation when applied to segment artificial structure images with unobvious boundaries and narrow regions. Therefore, an improved clustering segmentation algorithm to correct the segmentation results of SLIC is presented in this paper. The allocation of pixels is not only related to its own characteristic, but also to those of its surrounding pixels.Hence, it is appropriate to improve the standard SLIC through the pixels by focusing on boundaries. An improved SLIC method adheres better to the boundaries in the image is proposed, by using the first and second order difference operators as magnified factors. Experimental results demonstrate that the proposed method achieves an excellent boundary adherence for artificial structure images. The application of the proposed method is extended to images with an unobvious boundary in the Berkeley Segmentation Dataset BSDS500. In comparison with SLIC, the boundary adherence is increased obviously.展开更多
文摘针对常用的点对MRF(Markov random field)分割算法采用简单的先验模型,在对乳腺钼靶X图像中的乳腺肿块分割时产生的过分割问题,提出一种基于SLIC(simple linear iterative cluster)算法改进的MRF分割算法。采用SLIC算法将图像预分割为内部一致性较高的超像素区域,根据超像素区域的特征建立邻域系统并构建MRF,以超像素区域代替像素点作为分割单位实现乳腺肿块分割。实验结果表明,采用该算法对乳腺肿块进行分割可以高效获得较为准确的分割效果。
文摘为提高水体周边环境的变化检测结果的精度,提出一种改进的变化检测方法。在光谱与纹理特征结合的基础上融合指数特征构建混合特征空间,采用超像素生成算法(simple linear iterative cluster,SLIC)处理叠加影像获取地物对象,并综合地物对象的正反向异质信息构建地物对象的正反向异质性;使用最大数学期望算法与贝叶斯最小错误率理论获取两时相的变化信息,排除植被伪变化信息,形成相对准确和鲁棒的检测结果。试验结果表明:该方法能够有效区分水体周边环境中感兴趣的地物变化信息与不感兴趣的干扰信息、"伪变化信息"等,虚检率和漏检率较低,且正确率较高为96%以上,能够智能发现湖库水域周边"非正常"土地利用变化。
基金Supported by Defense Industrial Technology Development Program(JCKY2017602C016)
文摘Simple linear iterative cluster(SLIC) is widely used because controllable superpixel number, accurate edge covering, symmetrical production and fast speed of calculation. The main problem of the SLIC algorithm is its under-segmentation when applied to segment artificial structure images with unobvious boundaries and narrow regions. Therefore, an improved clustering segmentation algorithm to correct the segmentation results of SLIC is presented in this paper. The allocation of pixels is not only related to its own characteristic, but also to those of its surrounding pixels.Hence, it is appropriate to improve the standard SLIC through the pixels by focusing on boundaries. An improved SLIC method adheres better to the boundaries in the image is proposed, by using the first and second order difference operators as magnified factors. Experimental results demonstrate that the proposed method achieves an excellent boundary adherence for artificial structure images. The application of the proposed method is extended to images with an unobvious boundary in the Berkeley Segmentation Dataset BSDS500. In comparison with SLIC, the boundary adherence is increased obviously.