Description: 将提升小波用于目标跟踪。
小波提升的核心就是更新算法和预测算法,通过预测算法可以得到高频信息,而通过更新算子可以得到正确的低频信息.提升样式可以实现原位计算和整数提升,并且变换的中间结果是交织排列的.其中原位计算和整数提升在硬件实现中很有价值.-will enhance wavelet for target tracking. Lifting the core algorithm and is updated prediction algorithm, algorithm can be predicted high-frequency information, and by updating operator can correct the low-frequency information. upgrade format can achieve in-situ upgrade and Integer calculations, and transform the intermediate results are intertwined with a. which integer calculation and in-situ upgrading the hardware realization of great value. Platform: |
Size: 218235 |
Author:大仙 |
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Description: 将提升小波用于目标跟踪。
小波提升的核心就是更新算法和预测算法,通过预测算法可以得到高频信息,而通过更新算子可以得到正确的低频信息.提升样式可以实现原位计算和整数提升,并且变换的中间结果是交织排列的.其中原位计算和整数提升在硬件实现中很有价值.-will enhance wavelet for target tracking. Lifting the core algorithm and is updated prediction algorithm, algorithm can be predicted high-frequency information, and by updating operator can correct the low-frequency information. upgrade format can achieve in-situ upgrade and Integer calculations, and transform the intermediate results are intertwined with a. which integer calculation and in-situ upgrading the hardware realization of great value. Platform: |
Size: 218112 |
Author:大仙 |
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Description: 基于轮廓追踪的字符识别文章，把小波和分形维相结合-Based on the outline of the character recognition tracking articles, the wavelet and fractal dimension of combining Platform: |
Size: 281600 |
Author:王茜 |
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Description: 采用小波系数极大值跟踪法去除图像噪声,建立了尺度间小波系数极大值跟踪矩阵,标识出小波系
数极大值的信噪属性,剔除了噪声部分对应小波系数极大值,从而抑制了噪声污染-Maxima of wavelet coefficients using the tracking method to remove image noise, the establishment of inter-scale wavelet coefficient maxima tracking matrix, the wavelet coefficients logo letter maximum noise properties, removed some of the noise wavelet coefficients corresponding to a maximum value, thereby inhibiting noise pollution Platform: |
Size: 461824 |
Author:wangrui |
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Description: 在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。-The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved. Platform: |
Size: 4096 |
Author:李隆基 |
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Description: This method is used for tracking wavelet/optical flow-based
detection for automatic target recognition in the following paper:
Dessauer, M. and Dua S. “Wavelet-based optical flow object detection, motion estimation, and tracking on moving vehicles” Platform: |
Size: 1355776 |
Author:MemoSergey |
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Description: 本文讨论了小波神经网络在机动多目标跟踪中的应用,多目标跟踪就是主体为了维持对多个目标(客体)当前状态的估计而对所接收的量测信息进行处理的过程。以非线性大规模并行分布式处理为特征的神经网络可以解决传统的目标跟踪方法的难以解决的计算量组合爆炸问题以及需要确定机动目标的数学模型的问题, 将小波分析原理与神经网络相融合,提出了基于小波神经网络的目标跟踪方法来提高系统的学习能力、表达能力以及机动多目标状态的估计精度。-This article discusses the application of wavelet neural network in motorized multi-target tracking, multi-target tracking is the main measurement information received in order to maintain the current state of the multiple goals (guest) estimated processing. The nonlinear massively parallel distributed processing is characterized neural network can solve the traditional target tracking methods computational combinatorial explosion problem difficult to solve the mathematical model of the problem and the need to determine the maneuvering target, the principle of wavelet analysis and neural network fusion, target tracking method based on wavelet neural network to improve the system' s ability to learn, the ability to express and maneuvering multi-target state estimation accuracy. Platform: |
Size: 421888 |
Author:yaomeng |
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Description: 计算Haar小波特征，用AdBaoost提取部分有代表性的特征共三种特征选择方法与SVM相结合进行目标跟踪的算法。
-The calculated Haar wavelet features to extract some of the typical characteristics of three feature selection method combined with SVM algorithm for target tracking AdBaoost. Platform: |
Size: 12433408 |
Author:QQ |
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Description: In this paper, we describe an approach to the problem
of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part
of the algorithm is based on Bayesian wavelet denoising, which
has been chosen due to its exceptional ability to incorporate diversea prioriinformation into the process of image recovery Platform: |
Size: 2400256 |
Author:Linh |
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Description: 基于小波变换的正交匹配追踪算法及其应用先介绍了压缩感知的基本原理以及理论模型，然后详细阐述了匹配追踪(Ⅷ)以及正交匹配追踪(()脚)
两种重构算法，进而提出了基于小波变换的正交匹配追踪算法(wOⅧ)，即先将信号经过单层小波变换，保留信号的
低频部分，只对高频稀疏部分进行压缩，然后利用正交匹配追踪算法进行重构，最后对低频及处理后得到的高频部分
进行小波反变换得到重构信号。-Based on orthogonal wavelet transform matching tracking algorithm and its application to the basic principles and theoretical models compressed sensing, and then elaborated matching pursuit (Ⅷ) and orthogonal matching pursuit Platform: |
Size: 1202176 |
Author:徐梦飞 |
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