提出一种简化的核密度估计模型KDE(Kernel Density Estimation)动态背景建模算法,构建了基于ARM11的视频动态背景分割系统。不仅能大幅度降低传统KDE背景建模的所需的运算量和存储量,提高处理速度,而且还具有良好的运动前景分割效果。...提出一种简化的核密度估计模型KDE(Kernel Density Estimation)动态背景建模算法,构建了基于ARM11的视频动态背景分割系统。不仅能大幅度降低传统KDE背景建模的所需的运算量和存储量,提高处理速度,而且还具有良好的运动前景分割效果。系统完成了视频动态背景的建模、运动前景分割、视频显示、图片压缩保存等功能,具有较好的性价比。展开更多
A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions...A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions. Then different fusion rules are employed in different regions so that the target information is preserved as much as possible. In addition, steerable non-separable wavelet frame transform is used in the process of multi-resolution analysis, so the system achieves favorable characters of orientation and invariant shift. Compared with other image fusion methods, experimental results showed that the proposed method has better capabilities of target recognition and preserves clear background information.展开更多
文摘提出一种简化的核密度估计模型KDE(Kernel Density Estimation)动态背景建模算法,构建了基于ARM11的视频动态背景分割系统。不仅能大幅度降低传统KDE背景建模的所需的运算量和存储量,提高处理速度,而且还具有良好的运动前景分割效果。系统完成了视频动态背景的建模、运动前景分割、视频显示、图片压缩保存等功能,具有较好的性价比。
基金Project (No. 2004CB719401) supported by the National Basic Research Program (973) of China
文摘A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions. Then different fusion rules are employed in different regions so that the target information is preserved as much as possible. In addition, steerable non-separable wavelet frame transform is used in the process of multi-resolution analysis, so the system achieves favorable characters of orientation and invariant shift. Compared with other image fusion methods, experimental results showed that the proposed method has better capabilities of target recognition and preserves clear background information.
