【作者】 周文明；

【导师】 姚莉秀；

【作者基本信息】 上海交通大学 ， 检测技术与自动化装置， 2011， 硕士

【摘要】 显著性是当前计算机视觉领域的研究热点之一,它模拟人眼视觉注意和信息处理机制,设计类似的显著性计算模型。到目前为止,针对静态图像已有较为成熟的显著区域提取方法。显著性不考虑全局特征的变化,而重点关注局部特征对比,将其扩展到对于视频中运动目标的处理,能够避免在动态场景下建立运动补偿模型的难题。运动检测是计算机视觉领域的一个经典问题。高动态背景与相机运动是该任务的两个难点。空时显著性检测,已被应用到运动目标检测中,且被证明具有对高动态背景和相机运动鲁棒的特点。本文首先回顾了显著性算法的研究现状,这些算法可以笼统的分为3类:基于底层特征、基于图像复杂度和基于生物视觉模型的。然后阐述了与显著性有关的重要的生物学基础,从这些生物学上的工作机制,我们提出了显著性算法设计基本原则。本文也回顾了经典的运动目标检测算法,并提出了一种基于混合动态纹理的空时显著性方法来检测动态场景下的运动目标。本文的主要工作和创新点有:(1)综述了显著性算法研究的国内外进展,分析常用算法的优缺点和应用场景;(2)实现帧差法,W4,GMM等运动目标检测算法,并通过实验分析提出了改进;(3)基于人视觉系统的工作机理,本文总结已有的算法设计思想,并归纳为4条原则;(4)改进了一种基于混合动态纹理的空时显著性方法,并将其应用到运动目标检测之中。该方法首先利用混合动态纹理(MDT)对高动态背景进行建模,然后基于中心\邻域的框架利用时空信息计算显著性图。对显著性图进行适当的阈值处理,即得到运动目标检测结果。实验结果表明,本文提出的方法能够有效地改善运动目标检测的精度。更多还原

【Abstract】 Salience detection is one of the research focus in computer vision, which simulates human visual attention and vision information processing mechanisms to found saliency computation models. So far, for the static image, there have been many mature methods for saliency regional extraction. Saliency detection focuses on local feature contrast rather than global feature changes. So it can avoid the problem of motion compensation in dynamic scenes.Motion detection is a classical problem in computer vision research. Highly dynamic background and camera motion are two challenges of this task. Spatiotemporal saliency detection has been used in motion detection and is robust to the challenges.This paper reviews the saliency research achievement, and generally divided these algorithms into three categories: algorithms based on low-level features, algorithms based on image complexity and algorithms based on biological visual model. Then introduces some important biological basis related with saliency, based on which we propose 4 basic principles of saliency algorithm design. This article also reviews the classic motion detection algorithms, and presents a spatiotemporal saliency based on mixtures of textures which then is used to detect moving object in dynamic scenes.The main work and innovations are:(1) reviewed the significant progress in algorithm research at home and abroad, the advantages and disadvantages of commonly used algorithms and scenarios;(2)Implement some classic moving target detection algorithms, such as temporal difference, W4, and GMM, and make some improvements by analyzing our experiments;(3) Based on the mechanisms of human visual system, the paper summarizes 4 principles for the saliency algorithms design;(4)We present a novel spatiotemporal saliency detection method based on mixtures of textures(MDT), which is then used to resolve the motion detection problem. First we model the highly dynamic background by mixtures of textures. Then calculate the spatiotemporal saliency based on a center-surround framework. Finally the saliency map yields motion detection results after thresholding. The experiments demonstrate that our method can effectively promote the accuracy of motion detection.更多还原