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Multi-objects motion segmentation in image sequence based on cellular neural networks

机译:基于蜂窝神经网络的图像序列中的多对象运动分割

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In this paper, a new approach to multi-objects motion segmentation in image sequence based on cellular neural networks (CNN) is proposed. As the core of this approach, difference merged image algorithm is presented. In order to realize the algorithm, the reverse CNN template, the addition CNN template, the patch-filled CNN template and the composition CNN template are presented and designed. For based on CNN, this approach can improve the capability of real-time in motion segmentation. And on the other hand, because difference merged image algorithm we improved is directly used in gray-scale image processing instead of in binary image processing, it can get more information of motion and increase the accuracy of segmentation. Finally, we show the experiment results, which prove that this approach has a good capability in multi-objects motion segmentation.
机译:本文提出了一种基于蜂窝神经网络(CNN)的图像序列中的多对象运动分段的新方法。作为这种方法的核心,呈现了差异合并的图像算法。为了实现算法,呈现反向CNN模板,添加CNN模板,填充的CNN模板,填充的CNN模板和组合CNN模板。对于基于CNN,这种方法可以提高运动分割中实时的能力。另一方面,由于我们改进的差异合并图像算法直接用于灰度图像处理而不是在二进制图像处理中,可以获得更多的运动信息并提高分割的准确性。最后,我们展示了实验结果,证明这种方法在多对象运动分段中具有良好的能力。

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