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
为解决点刻式直接零件标志(Direct part mark,DPM)码基本单元分割困难、区域定位欠精确等问题,提出使用超像素分割和谱聚类相结合的算法,对含有DPM区域的图像进行初步分割和精确定位.首先为提高超像素分割的准确、快速和完整性,本文利...为解决点刻式直接零件标志(Direct part mark,DPM)码基本单元分割困难、区域定位欠精确等问题,提出使用超像素分割和谱聚类相结合的算法,对含有DPM区域的图像进行初步分割和精确定位.首先为提高超像素分割的准确、快速和完整性,本文利用近邻传播聚类思想实现自动聚类得到超像素区域,并引入边缘置信度调整超像素边缘,形成自适应边缘简单线性迭代聚类(Adaptive edge simple linear iterative clustering,AE-SLIC)算法.该算法改进了简单线性迭代聚类(Simple linear iterative clustering,SLIC)超像素分割算法存在的未明确界定超像素区域边缘信息和分割数目无法自适应确定等问题;其次,将超像素作为谱聚类中图的顶点进行二次聚类,DPM区域内超像素因相似度高而被聚集为一类,从而完成点刻式DPM区域的精确定位.经实验测试和分析,本文算法得到的超像素分割结果在完整性、运算复杂度等方面优于常见的超像素分割算法.与基于像素点运算的传统定位算法相比,本文算法具有良好的实时性、定位准确率和鲁棒性.展开更多
针对无人机可见光图像目标小、对比度弱的问题,本文提出一种基于简单线性迭代聚类(Simple linear iterative clustering,SLIC)分层分割的极小目标检测方法。首先使用预处理方法提高原始图像的对比度,并利用Top-hat融合方法进行初始分割...针对无人机可见光图像目标小、对比度弱的问题,本文提出一种基于简单线性迭代聚类(Simple linear iterative clustering,SLIC)分层分割的极小目标检测方法。首先使用预处理方法提高原始图像的对比度,并利用Top-hat融合方法进行初始分割以确定目标区域,其次利用SLIC方法完成目标精细分割,并采用改进的具有噪声的基于密度的聚类方法(Density-based spatial clustering of applications with noise,DBSCAN)对SLIC分割结果进行超像素聚类,最后提取目标的邻域熵等多种底层特征,使用特征匹配方式检测目标,获取最终检测结果。本文提出了一种全局检测和局部检测相结合的检测策略,极大提高了检测速度。仿真结果表明,本文方法可以有效提高无人机小目标的检测性能,加速检测速度。展开更多
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.
文摘为解决点刻式直接零件标志(Direct part mark,DPM)码基本单元分割困难、区域定位欠精确等问题,提出使用超像素分割和谱聚类相结合的算法,对含有DPM区域的图像进行初步分割和精确定位.首先为提高超像素分割的准确、快速和完整性,本文利用近邻传播聚类思想实现自动聚类得到超像素区域,并引入边缘置信度调整超像素边缘,形成自适应边缘简单线性迭代聚类(Adaptive edge simple linear iterative clustering,AE-SLIC)算法.该算法改进了简单线性迭代聚类(Simple linear iterative clustering,SLIC)超像素分割算法存在的未明确界定超像素区域边缘信息和分割数目无法自适应确定等问题;其次,将超像素作为谱聚类中图的顶点进行二次聚类,DPM区域内超像素因相似度高而被聚集为一类,从而完成点刻式DPM区域的精确定位.经实验测试和分析,本文算法得到的超像素分割结果在完整性、运算复杂度等方面优于常见的超像素分割算法.与基于像素点运算的传统定位算法相比,本文算法具有良好的实时性、定位准确率和鲁棒性.
文摘针对无人机可见光图像目标小、对比度弱的问题,本文提出一种基于简单线性迭代聚类(Simple linear iterative clustering,SLIC)分层分割的极小目标检测方法。首先使用预处理方法提高原始图像的对比度,并利用Top-hat融合方法进行初始分割以确定目标区域,其次利用SLIC方法完成目标精细分割,并采用改进的具有噪声的基于密度的聚类方法(Density-based spatial clustering of applications with noise,DBSCAN)对SLIC分割结果进行超像素聚类,最后提取目标的邻域熵等多种底层特征,使用特征匹配方式检测目标,获取最终检测结果。本文提出了一种全局检测和局部检测相结合的检测策略,极大提高了检测速度。仿真结果表明,本文方法可以有效提高无人机小目标的检测性能,加速检测速度。
