destination object

destination object的基本解释

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目标对象

与destination object相关的网络例句 [注：此内容来源于网络，仅供参考]

In this paper, the theory, algorithm, and experiment of automatic object detection and tracking are studied in depth. It is firstly pointed out that the essential of Mean Shift method is a special Newton-Gaussian method. A new method named Fast Mean Shift is established to stretch the conservative step of Mean Shift method. The convergence and validity of this new method are proved in theory. And it is also proved that the convergence speed of Fast Mean Shift is faster than that of Mean Shift. The contrast experiments of searching the maximum possibility density of random of data sets in plane and 3D space are done. The results show that this new method can reduce the iterations greatly. A new object tracking method based on Fast Mean Shift is built to improve the object tracking performance, which is shown in the face tracking experiment with the tennis sequence provided by the Ohio State University, and the car tracking experiment with the car sequence provided by Kalsruhe University. The face trcking experiment with highly noised images show that the object tracking method based on Fast Mean Shift has strong anti-jamming ability. A new fast color object detection technology based on characteristic color is established, which use characteristic color distribution to compute the characteristic color vector of any area in an image quickly. With the high performance search method, the fast object detection is achieved. At last, using object tracker based on Fast Mean Shift and color object detector based on characteristic color with the Kalman filter, PID controller, searial communication and other technologies, automatic object detection and tracking system with control system is built. The availability and anti-jamming ability of this system are verified by some object detection and tracking tests in different scenes.

本文对目标自动识别与跟踪进行了理论、计算、试验三方面的深入研究，主要包括：首次指出了目标跟踪技术中常用的均值迁移方法的本质为一种特殊的高斯-牛顿方法，改进了均值迁移方法步长取值保守的弱点，建立了快速均值迁移方法，证明了该方法的收敛性、有效性以及收敛速度优于均值迁移方法；进行了平面和3维随机分布数据集的最大概率密度搜索对比试验，试验结果表明，快速均值迁移方法大大减少了迭代次数；建立了基于快速均值迁移的目标跟踪方法，利用俄亥俄州立大学提供的乒乓球序列图像和卡斯鲁厄大学的汽车序列图像，对人脸和汽车目标跟踪性能分别进行了对比试验，并进行了高噪声人脸图像目标跟踪试验，结果表明，基于快速均值迁移的目标跟踪方法有效提高了目标跟踪性能，具有很强的抗干扰能力；建立了一种新型彩色目标自动识别方法，采用特征色彩分布函数实现了对任意图像区域特征色彩矢量的快速计算，建立了高效的搜索方法，实现了彩色目标的快速识别；将基于快速均值迁移方法的目标跟踪方法、基于特征色彩的目标识别方法与卡尔曼滤波、PID控制、串行通讯等技术结合，建立了带有控制系统的快速目标自动识别与跟踪系统，并在不同场景下进行了目标自动识别与跟踪试验，验证了快速目标自动识别与跟踪系统的有效性和抗干扰能力。

This thesis is on the foundation of the comb of destination marketing literature in the domestic and international studies. From the key concept of strategic marketing "value", the destination is a platform where value is created and transferred. Regarding the management process of destination marketing strategy as the noumenon, through the analysis of value theory, epistemology and methodology, the text constructs the destination marketing strategy theory.

本文在对国内外目的地营销文献梳理的基础上，从战略营销的核心概念"价值"入手，视目的地为一个价值创造和实现的平台，以目的地营销管理过程为本体，重点通过对目的地营销战略的价值论、认识论、方法论和运行平台的分析和论述来构建旅游目的地营销战略理论。

This version expects its destination to be raw, unconstructed memory. Rather than assigning elements from the input range to the destination, it copy-constructs each element in the destination. The copy constructor for T is used to copy each element from the input range to the destination.

这个版本希望目的地是原始的未构造内存，它在目的地复制构造每个元素，而不是将输入范围的元素赋值给目的地，使用 T 的复制构造函数从输入范围将每个元素复制到目的地。

与destination object相关的网络解释 [注：此内容来源于网络，仅供参考]

destination object：目的对象

descendant 后代 | destination object 目的对象 | device 设备

destination object：目标对象

620destination document目标文件 | 621destination object目标对象 | 622destination page目标页