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运动视觉
    Kalman Filter-Based Algorithm for Recovery of Dynamical Scene Structure in Motion Vision
    基于Kalman滤波器运动视觉景物结构复原算法
    Uncertainty modeling for motion vision system
    运动视觉系统误差建模
    Accurate Moving Objects Extraction and Tracking in Motion Vision
    运动视觉中目标的精确提取与跟踪技术
    The state-of-the-art of the motion vision analysis and stereovision system is summarized. The existing problem and development trend, which are related with machine vision, are presented.
    综述了运动视觉分析和立体视觉系统的研究概况,指出机器视觉相关研究存在的问题与发展趋势,并给出论文的主要研究内容。
    Motion vision analysis is studied in detail.
    对运动视觉分析进行了详细的研究。
    In this dissertation, accurate moving objects extraction and tracking in motion vision are studied in depth, including motion detection, shadow suppression, contour extraction of moving objects, and objects tracking.
    本文深入研究了运动视觉中目标的精确提取与跟踪算法,包括运动检测、阴影消除、外轮廓提取以及目标的跟踪四个方面。
    In the study of computer vision, the measure of scene depth information mostly bases on focused image, the main method is stereoscopic vision and motion vision.
    在计算机视觉研究中,景物深度信息的测量大都是基于聚焦图像进行的,采用方法也多以立体视觉和运动视觉为主。
    In general, the human motion vision analysis includes the follow four steps: motion detection, moving object classification, people tracking, motion understanding and description.
    人体运动视觉分析一般包含运动检测,运动目标分类,人体跟踪,行为理解与描述四个阶段。
    This paper builds up first dynamical systemmodel of scene variances in motion vision. Then.
    本文首先建立了运动视觉景物结构变化的动态系统模型,然后借助于此模型产生可最优估计图象点动态深度的Kalman 滤波器.
    This paper proposes a robust and reliable algorithm which can be used to detect image motion in optical flow and motion discontinuitics effcetively using local uniform operations and correlation--based motion detector. This algorithm will open a new way of furture research on dynamic motion vision.
    本文基于局部均匀操作及相关运动检测器提出一种能够有效地检测光流图象运动及运动间断的强而可靠的算法,从而为进一步研究动态运动视觉开辟了新的途经.
 

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