Due to the limitation of Depth Of Field (DOF) of microscope, the regions which are not within the DOF will be blurring after imaging. Thus for micro-image fusion, the most important step is to identify the blurring re...Due to the limitation of Depth Of Field (DOF) of microscope, the regions which are not within the DOF will be blurring after imaging. Thus for micro-image fusion, the most important step is to identify the blurring regions within each micro-image, so as to remove their undesirable impacts on the fused image. In this paper, a fusion algorithm based on a novel region growing method is proposed for micro-image fusion. The local sharpness of micro-image is judged block by block, then blocks whose sharpness is lower than an adaptive threshold are used as seeds, and the sharpness of neighbors of each seed are evaluated again during the region growing until the blurring regions are identified completely. With the decreasing in block size, the obtained region segmentation becomes more and more accurate. Finally, the micro-images are fused with pixel-wise fusion rules. The experimental results show that the proposed algorithm benefits from the novel region segmentation and it is able to obtain fused micro-image with higher sharpness compared with some popular image fusion method.展开更多
文摘针对海量图像数据中目标的分割及识别问题,提出了一种自适应控制下图像分割及并行挖掘算法.采用隶属度函数窗口宽度在图像直方图控制下自适应调整模糊阈值图像分割方法对图像进行分割,提取出感兴趣的潜在目标区域,基于共轭梯度法改进的BP神经网络算法对潜在的目标区域进行训练和识别,识别算法基于OpenMP并行处理模型开发来提高执行效率.结果表明:本文算法相对于基于偏移场的模糊C均值、灰度波动变换自适应阈值和自适应最小误差阈值具有更高的分割准确率,与传统神经网络算法的识别结果相比,平均识别率提高了8%,运行时间减少了2. 5 s.
文摘为了提高潜孔钻机定位的精度,提出采用双目立体视觉技术获得潜孔钻机定位过程中孔位标识的三维位置信息,用于指导潜孔钻机进行自动钻孔定位.首先利用双目摄像机获取含有孔位标识的图像对,然后采用隶属度函数为梯形函数的模糊阈值分割方法对孔位标识进行识别分割,利用图像的不变矩原理求取孔位标识的质心,并通过三角测量原理求取孔位的空间位置信息.最后,在潜孔钻机试验台上进行了基于双目立体视觉系统的自动定位试验.结果表明,所采用以灰度直方图为基础的模糊阈值分割能够实现对孔位标识有效信息的提取,且定位误差小于25 mm.
基金Supported by the Natural Science Foundation of Zhejiang Province (Y1101240)Zhejiang Scientific and Technical Key Innovation Team (2010R50009)+1 种基金Natural Science Foundation of Ningbo (2011A610200, 2011A610197)Student Research and Innovation Training Program of Zhejiang Province (New-shoot Talents Project 2011R-405054) (A00162100400)
文摘Due to the limitation of Depth Of Field (DOF) of microscope, the regions which are not within the DOF will be blurring after imaging. Thus for micro-image fusion, the most important step is to identify the blurring regions within each micro-image, so as to remove their undesirable impacts on the fused image. In this paper, a fusion algorithm based on a novel region growing method is proposed for micro-image fusion. The local sharpness of micro-image is judged block by block, then blocks whose sharpness is lower than an adaptive threshold are used as seeds, and the sharpness of neighbors of each seed are evaluated again during the region growing until the blurring regions are identified completely. With the decreasing in block size, the obtained region segmentation becomes more and more accurate. Finally, the micro-images are fused with pixel-wise fusion rules. The experimental results show that the proposed algorithm benefits from the novel region segmentation and it is able to obtain fused micro-image with higher sharpness compared with some popular image fusion method.
