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Algorithm based on super low resolution video image processing

基于超低分辨率视频图像处理的算法

基本信息

批准号：
17500124
负责人：
NAKAMURA Eiji
金额：
$ 1.84万
依托单位：
Aichi Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17500124/
关键词：
Video image Processing Motion estimation Insect vision EMD ERD リアルタイム動き検出

项目摘要

In the present video image processing community, people tend to utilize computational resources as much as possible rather than try to reduce the computational complexity of a given motion estimation algorithm for realizing real-time image processing. In our research, insects with very simple neural systems, capable of controlling their flight maneuvers in real time are focused and their computational models, so called an elementary motion detector (EMD) and its derivative an elaborated Reichardt detector (ERD), are theoretically analyzed for developing an motion estimation algorithm of low computational complexity to which very low resolution video images are fed in real time.In our research, it has been found that (i) estimation results are a function of filter parameters included in these models, (ii) estimation accuracy can be improved greatly by optimizing the filter parameters, and (iii) such an optimization can be done in a combination of analytical and numerical procedures. The performance of our developed algorithms based on the above numerical modes was examined by implementing them in software programs and hardware resources. The algorithms can estimate motion successfully, although in certain limited conditions, in real time utilizing very low resolution video image sequences.

在目前的视频图像处理领域，人们倾向于尽可能多地利用计算资源，而不是试图降低给定运动估计算法的计算复杂度以实现实时图像处理。在我们的研究中，昆虫具有非常简单的神经系统，能够真实的控制它们的飞行动作，并且它们的计算模型，即所谓的基本运动检测器（EMD）及其衍生物-精细Reichardt检测器（ERD），被理论上分析，以发展一种低计算复杂度的运动估计算法，真实的地馈送非常低分辨率的视频图像。已经发现，（i）估计结果是包括在这些模型中的滤波器参数的函数，（i i）通过优化滤波器参数可以极大地提高估计精度，以及（iii）这种优化可以在分析和数值过程的组合中完成。通过在软件程序和硬件资源中实现它们，我们开发的算法基于上述数值模式的性能进行了检查。该算法可以成功地估计运动，虽然在某些有限的条件下，在真实的时间利用非常低的分辨率视频图像序列。