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:通过对梯度矢量流(GVF)力场迭代和轮廓收敛过程中时间开销大、不能逼近较复杂轮 廓等不足之处的分析,提出了一种新的初始轮廓线的设置方法。基于小波变换的多尺度轮廓检测算 法,能够较精确地定位出各类轮廓,在此基础上设置的初始轮廓线能够非常好地靠近图像中的各种真 实轮廓。实验结果表明,此方法有效地缩小了搜索的范围,减少了GVF迭代次数,提高了轮廓收敛的 速度,并保留了GVF Snake模型的所有优点。-: The gradient vector flow (GVF) field iterative convergence process and outline the time cost, could not approach the more complex contours of such deficiencies, a new method of setting the initial contour. Wavelet-based multi-scale contour detection algorithm can accurately locate the various types of contours, on the basis set to a very good initial contour close to the image in a variety of real contours. Experimental results show that this method effectively reduces the search and reduce the number of iterations of the GVF to improve the convergence speed profile, and retained all the advantages of GVF Snake model.
Date : 2025-12-29 Size : 243kb User : fuky

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本文提出了一种复杂条件下基于子空间梯度方向直方图跟踪的方法,通过大量样本的离线训练构建目标的投影子 空间,并用梯度方向直方图在子空间的投影作为新的目标描述特征.为了满足实时性的要求,采用积分直方图方法 提高粒子特征的计算速度;然后结合粒子滤波方法在子空间中计箅粒子与训练样本集之间的相似度,进而估计目标 的运动参数.实验结果表明,该方法能够在光照变化、噪声干扰、模糊、目标姿态和尺度改变,以及部分遮捎等恶劣条 件下实现准确跟踪,比传统的跟踪方法具有更高的跟踪精度和跟踪鲁棒性,能够满足地面侦察任务在多种复杂条件下对感兴趣目标进行准确跟踪的需求.-This paper presents a complex condition gradient orientation histogram based on subspace tracking methods, through a large number of samples offline training build target subspace projection and gradient orientation histogram in subspace projection as a new target descriptive characteristics. In order to meet the requirements of real-time, using integral histogram method improves the computing speed of the particle characteristics then combined with particle filter in subspace meter grate particles and the similarity between the training sample set, and then estimate the target motion parameters. Experimental results show that this method can change in the light, noise, blur, target attitude and scale changes, and take along some cover in bad condition for accurate tracking, than the traditional tracking method has higher tracking accuracy and robustness to meet the ground reconnaissance missions in a variety of complex conditions accurately track targets of interest requirements.
Date : 2025-12-29 Size : 542kb User : wenping
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