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Description: 基于Bhattacharyya距离准则的核空间特征提取算法，觉得不错-Bhattacharyya distance criteria based on the nuclear space feature extraction algorithm think it's good
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Size: 231105 |
Author: 王位 |
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Description: 基于Bhattacharyya距离准则的核空间特征提取算法，觉得不错-Bhattacharyya distance criteria based on the nuclear space feature extraction algorithm think it's good
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Size: 230400 |
Author: 王位 |
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Description: A new approach toward target representation and localization, the central component in visual tracking
of non-rigid objects, is proposed. The feature histogram based target representations are regularized
by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking .
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Author: |
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Description: 提出一种新的目标表示和定位方法,该方法是非刚体跟踪的核心技术.利用均质空间掩膜规范基于特征直方图的目标表示,该掩膜引入了适合于梯度优化的空间平滑相似函数,所以可以将目标定位问题转换为局部极大值求解问题.我们利用从Bhattacharyya系数倒出的规则作为相似度量,利用mean shift procedure完成优化求解.在给出的测试用例中, 本文方法成功解决了相机移动,阴影,以及其他的图象噪声干扰.文章对运动滤波和数据关联技术的集成也进行了讨论.-A new objective and positioning method to track non-rigid body' s core technology. Standardizing the use of homogeneous space mask the characteristics of histogram based on the objectives that the mask is suitable for the introduction of gradient optimization is similar to spatial smoothing function, Therefore, targeting the problem can be converted to solve the problem of local maxima. We poured from the rules of Bhattacharyya coefficient as similarity measure, using mean shift procedure for solving optimization. give the test cases in, the method succeeded in solving the camera Mobile, shadows, and other image noise. article on the campaign filtering and data association techniques of integration were also discussed.
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Size: 2700288 |
Author: maolei |
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Description: 论文：基于颜色信息的粒子滤波算法实现目标跟踪。利用颜色直方图的巴氏距离。-Thesis: Based on the color information of the particle filter target tracking implementation. Using color histogram Bhattacharyya Distance.
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Size: 224256 |
Author: hf |
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Description: compute Bhattacharyya distance between two Gaussian classes
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Size: 1024 |
Author: aseman |
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Description: MATLAB bhattacharyya 距离计算-MATLAB bhattacharyya distance
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Size: 2048 |
Author: cara |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.
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Size: 2459648 |
Author: Ali |
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Description: this Bhattacharyya Distances Calculation Codes-this is Bhattacharyya Distances Calculation Codes
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Size: 2048 |
Author: Mohammad |
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Description: usefull file designed in matlab
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Size: 84992 |
Author: zeyad |
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Description: Opencv images matching by Correlation, ChiSqr, Intersect and Bhattacharyya methods
-Opencv images matching by Correlation, ChiSqr, Intersect and Bhattacharyya methods
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Size: 11264 |
Author: mike_s |
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Description: Bhattacharyya 直方图对比系数-Bhattacharyya is good
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Size: 1024 |
Author: 吴良健 |
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Description: This paper presents a method that combines
colour and motion information to track pedestrians in video sequences captured by a fixed camera. Pedestrians are firstly
detected using the human detector proposed by Dalal and Triggs which involves computing the histogram of oriented gradients descriptors and classification using a linear support
vector machine. For the colour-based model, we extract a 4-dimensional colour histogram for each detected pedestrian window and compare these colour histograms between consecutive
video frames using the Bhattacharyya coefficient. For the motion model, we use a Kalman filter which recursively predicts and updates the estimates of the positions of pedestrians in the video frames. We evaluate our tracking method using videos from two pedestrian video datasets from the
web. Our experimental results show that our tracking method outperforms one that uses only colour information and can handle partial occlusion.
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Size: 484352 |
Author: linuszhao |
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Description: 用卡尔曼滤波和自适应窗口的均值偏移算法再结合Bhattacharyya系数粗定位实现视频目标跟踪-Kalman filtering and adaptive window mean shift algorithm combined with coarse positioning Bhattacharyya coefficient for video tracking
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Size: 11788288 |
Author: HUBO |
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Description: 一个比例和方向自适应均值漂移跟踪算法（SOAMST）
提出本文所要解决的问题，如何估计的规模和方向
改变均值漂移下的目标跟踪框架。在原来的均值偏移
跟踪算法，可以很好地估计目标的位置，规模的同时，
方向的变化，不能自适应估计。考虑到图像（重量）
是来自于目标运动模型和候选模型可以代表的可能性，一个
像素属于目标，我们证明了原来的均值漂移跟踪算法可以
推导出的重量图像的零阶和一阶矩。随着零阶
矩和目标模型和候选模型之间的Bhattacharyya系数，
提出了简单而有效的方法来估计的规模为目标。然后一种方法，
利用估计的区域和第二阶中心矩，提出
自适应地估计目标的宽度，高度和方向的变化。广泛
实验来证实所提出的方法，并验证其可靠性
规模和方向变化的目标。-A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is
proposed in this paper to address the problem of how to estimate the scale and orientation
changes of the target under the mean shift tracking framework. In the original mean shift
tracking algorithm, the position of the target can be well estimated, while the scale and
orientation changes can not be adaptively estimated. Considering that the weight image
derived from the target model and the candidate model can represent the possibility that a
pixel belongs to the target, we show that the original mean shift tracking algorithm can be
derived using the zeroth and the first order moments of the weight image. With the zeroth order
moment and the Bhattacharyya coefficient between the target model and candidate model, a
simple and effective method is proposed to estimate the scale of target. Then an approach,
which utilizes the estimated area and the second order center moment, is proposed to
adaptively e
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Size: 2013184 |
Author: 吴盈 |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.
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Size: 2439168 |
Author: Felix |
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Description: matlab语言编写写的求两个相同大小彩色图像HSV空间的巴氏距离-matlab language written request two HSV color image the same size space Bhattacharyya distance
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Size: 33792 |
Author: 吴亮 |
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Description: 用于计算bhattacharyya距离，用于特征基因求取-
Bhattacharyya used to calculate the distance for feature gene strike
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Size: 1024 |
Author: 越萌 |
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Description: Color can provide an efficient visual feature for tracking nonrigid
objects in real-time. However, the color of an object can vary over
time dependent on the illumination, the visual angle and the camera
parameters. To handle these appearance changes a color-based target
model must be adapted during temporally stable image observations.
This paper presents the integration of color distributions into particle
filtering and shows how these distributions can be adapted over time. A
particle filter tracks several hypotheses simultaneously and weights them
according to their similarity to the target model. As similarity measure
between two color distributions the popular Bhattacharyya coefficient is
applied. In order to update the target model to slowly varying image
conditions, frames where the object is occluded or too noisy must be
discarded.
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Size: 226304 |
Author: yangs |
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Description: Error Bounds for Normal Densities, computationally simpler,slightly less tight bound
When the two covariance matrices are equal, k(1/2) is te same as the Mahalanobis distance between the two means
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Size: 3616768 |
Author: vidi
|
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