【作者】 于江有；

【导师】 王知衍；

【作者基本信息】 华南理工大学 ， 计算机应用技术， 2010， 硕士

【摘要】 多目标跟踪一直都是图像处理、计算机视觉和模式识别领域里研究的热点。在采集的图像序列中完成多目标跟踪涉及到各帧图像中目标的检测提取、重叠目标的分割、目标特征分析、目标预测与数据关联等内容。随着计算机技术的发展,多目标检测与跟踪的应用越来越广泛,已经渗透到工业、医疗保健、航空航天、军事等各个领域,在国民经济中发挥着越来越大的作用。计算机辅助精液分析(CASA)是多目标跟踪技术在医学领域的典型应用,它提供了精子动力学的量化数据,并且速度快、简单、快捷、实验结果可重复、较人工精子检测方法具有高精度、数据可靠等优点,正是本课题的研究意义所在。本文首先介绍了计算机辅助精子分析系统及应用现状,分析了国内外的系统的优势和存在问题,然后从两个方面讨论了几种经典的多目标跟踪算法(区域跟踪法、光流法、核跟踪法、轮廓跟踪法、特征匹配法、卡尔曼滤波和粒子滤波等),阐述了上述算法的基本原理,并分析了各自算法的优缺点和应用范围。精子的视觉特征和运动信息是精子多目标跟踪的重要指标,本文根据精子视觉特征和运动信息,和实际应用中遇到的各种困难,利用矩量保持和Otsu方法对视频序列进行目标分割,用自适应滤波对运动目标进行预测,然后利用改进的模板匹配和特征匹配的算法对目标进行图像配准,得到了精子目标的运动轨迹。本文对算法进行了仿真实验,实验结果降低计算机辅助精子分析系统的平均识别时间,提高了精确度。最后对本课题研究进行了总结,并指出了下一步研究的主要方向。更多还原

【Abstract】 Multiple Object detection and tracking related to computer image processing, pattern recognition, artificial intelligence and other areas, is widely applied in many aspects such as military, industrial, life, and so on.The computer-aided sperm analysis (CASA) is important application of the image analysis technique in the biological and medical area. It provides a rapid and automated assessment of the parameters of sperm motion, together with improved standardization and quality control.This paper summarizes and classifies two major components of a visual tracking system; target representation and localization and filtering and data association. Target representation and localization is mostly a bottom-up process. Typically the computational complexity for these algorithms is low. Filtering and data association is mostly a top-down process, which involves incorporating prior information about the scene or object, dealing with object dynamics, and evaluation of different hypotheses. The computational complexity for these algorithms is usually much higher.Examines pros and cons of tracking system and based on the sperm movement characteristics, Otsu and feature matching key technologies, the paper presents an efficient and low-cost method for automatically detecting and tracking the moving object from sperm image sequences. This tracking algorithm was used after the background and extra particles were successfully removed through a two-step enhancement algorithm. The algorithm improves the effectiveness and accuracy of sperm detecting.更多还原