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[Game Hook Crackbwh-1.5

Description: 魔兽世界瞬移外挂Bubba s Warcraft Hack -EverQuest world Shunyi store Bubba's Warcraft Hack
Platform: | Size: 731353 | Author: hxy | Hits:

[Game Hook Crackbwh-1.5_source

Description: 魔兽世界 bwh外挂 源代码 可以瞬移,值得研究
Platform: | Size: 740078 | Author: wori | Hits:

[Game Hook Crackbwh-1.5

Description: 魔兽世界瞬移外挂Bubba s Warcraft Hack -EverQuest world Shunyi store Bubba's Warcraft Hack
Platform: | Size: 731136 | Author: hxy | Hits:

[Game Hook Crackbwh-1.5_source

Description: 魔兽世界 bwh外挂 源代码 可以瞬移,值得研究-World of Warcraft plug-in source code can be BWH瞬移, worthy of study
Platform: | Size: 740352 | Author: wori | Hits:

[Hook apiinlinehookAndADE32

Description: inline hook & ADE 32(反汇编引擎),可用于动态的inline hook到任何内核函数。-inline hook and ADE 32
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[Game Hook Crackwow_source

Description: bwh-1.5_source WOW 源码-bwh-1.5_source wow source
Platform: | Size: 734208 | Author: fan | Hits:

[3D GraphicCBWH_IET_Computer-Vision

Description: 背景加权直方图算法(BWH)在[2]中提出了尝试 减少干扰的背景均值漂移跟踪的目标定位。然而, 在本文中,我们证明了权重分配给候选目标区域的像素 BWH是那些没有背景资料成正比,即不会引入BWH 任何新的信息,因为均值漂移迭代公式是不变的规模 改造砝码。然后,我们提出了一个校正BWH(CBWH)的公式 只转型的目标模式,但不是目标候选模型。 CBWH计划 可以有效地降低背景的干扰,在目标定位。实验 结果表明,CBWH可能会导致更快的收敛速度和更准确的定位比 通常的目标表示均值漂移跟踪。即使目标没有得到很好的初始化, 该算法仍然强劲跟踪的对象,这是很难实现由 传统的目标表示。-The background-weighted histogram (BWH) algorithm proposed in [2] attempts to reduce the interference of background in target localization in mean shift tracking. However, in this paper we prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, i.e. BWH does not introduce any new information because the mean shift iteration formula is invariant to the scale transformation of weights. We then propose a corrected BWH (CBWH) formula by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background’s interference in target localization. The experimental results show that CBWH can lead to faster convergence and more accurate localization than the usual target representation in mean shift tracking. Even if the target is not well initialized, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventiona
Platform: | Size: 730112 | Author: 吴盈 | Hits:

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