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首页> 外文会议>International Conference on Computational Science and Its Applications(ICCSA 2004) pt.4; 20040514-20040517; Assisi; IT >Feature Extraction and Correlation for Time-to-impact Segmentation Using Log-Polar Images
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Feature Extraction and Correlation for Time-to-impact Segmentation Using Log-Polar Images

机译：使用对数极化图像进行时间分割的特征提取和相关

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摘要

In this article we present a technique that allows high-speed movement analysis using the accurate displacement measurement given by the feature extraction and correlation method. Specially, we demonstrate that it is possible to use the time to impact computation for object segmentation. This segmentation allows the detection of objects at different distances. There are several methods to measure movement in front of a mobile vehicle (robot) equipped with a camera. Some methods detect movement from the analysis of the optical flow, while other methods detect movement from the displacement of objects or part of the objects (corners, edges, etc). Those methods based on the optical flow are suitable for high speed analysis (say 25 images per second) but they are not very accurate and treat the image as a whole, being it difficult to separate different objects in the scene. Those methods based on image feature extraction are good for object recognition and clustering, that can be more precise than other methods, but they usually require many calculations to yield a result, making it difficult to implement these methods in a navigation system of a robot or mobile vehicle.
机译：在本文中，我们提出了一种技术，该技术可以使用特征提取和相关方法给出的精确位移测量值来进行高速运动分析。特别地，我们证明了可以利用时间来影响对象分割的计算。这种分割允许检测不同距离的物体。有几种方法可以测量配备了摄像头的移动车辆（机器人）的运动。一些方法通过分析光流来检测运动，而其他方法则通过对象或对象的一部分（角，边缘等）的位移来检测运动。这些基于光流的方法适用于高速分析（例如每秒25张图像），但它们不太准确，无法将图像作为一个整体进行处理，因为很难分离场景中的不同对象。这些基于图像特征提取的方法非常适合对象识别和聚类，比其他方法更为精确，但是它们通常需要进行大量计算才能得出结果，因此很难在机器人或机器人的导航系统中实现这些方法。移动车辆。

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