Search - bhattacharyya

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[Other resource] 基于Bhattacharyya距离准则的核空间特征提取算法 DL : 0: 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
Update : 2008-10-13 Size : 225.69kb Publisher : 王位
[Other] 基于Bhattacharyya距离准则的核空间特征提取算法 DL : 0: 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
Update : 2025-04-26 Size : 225kb Publisher : 王位
[Special Effects] KernelTracking DL : 0: 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 .
Update : 2025-04-26 Size : 2.65mb Publisher :
[Graph Drawing] Tracking DL : 0: 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.
Update : 2025-04-26 Size : 2.58mb Publisher : maolei
[Documents] AAA DL : 0: 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.
Update : 2025-04-26 Size : 219kb Publisher : hf
[Algorithm] bhattacharyya DL : 0: compute Bhattacharyya distance between two Gaussian classes
Update : 2025-04-26 Size : 1kb Publisher : aseman
[Special Effects] bhattacharyya DL : 0: MATLAB bhattacharyya 距离计算-MATLAB bhattacharyya distance
Update : 2025-04-26 Size : 2kb Publisher : cara
[Industry research] KernelBasedObjectTracking DL : 0: 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.
Update : 2025-04-26 Size : 2.35mb Publisher : Ali
[Windows Develop] B_Bhattacharyya DL : 0: this Bhattacharyya Distances Calculation Codes-this is Bhattacharyya Distances Calculation Codes
Update : 2025-04-26 Size : 2kb Publisher : Mohammad
[Other] bhattacharyya DL : 0: usefull file designed in matlab
Update : 2025-04-26 Size : 83kb Publisher : zeyad
[OpenCV] kitai1 DL : 0: Opencv images matching by Correlation, ChiSqr, Intersect and Bhattacharyya methods -Opencv images matching by Correlation, ChiSqr, Intersect and Bhattacharyya methods
Update : 2025-04-26 Size : 11kb Publisher : mike_s
[Windows Develop] Bhattacharyya DL : 0: Bhattacharyya 直方图对比系数-Bhattacharyya is good
Update : 2025-04-26 Size : 1kb Publisher : 吴良健
[Software Engineering] Multiple-Pedestrian-Tracking-using-Colour-and-Mot DL : 0: 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.
Update : 2025-04-26 Size : 473kb Publisher : linuszhao
[OpenCV] BAH1 DL : 0: 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
Update : 2025-04-26 Size : 11.24mb Publisher : HUBO
[3D Graphic] IET_CV_SOAMST_2011 DL : 0: 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
Update : 2025-04-26 Size : 1.92mb Publisher : 吴盈
[Other] kernel_based_object_tracking_survey DL : 0: 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.
Update : 2025-04-26 Size : 2.33mb Publisher : Felix
[Special Effects] GetHSVSimilarity DL : 0: 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
Update : 2025-04-26 Size : 33kb Publisher : 吴亮
[matlab] Bhattacharyya DL : 0: 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
Update : 2025-04-26 Size : 1kb Publisher : 越萌
[Other] Object-Tracking DL : 0: 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.
Update : 2025-04-26 Size : 221kb Publisher : yangs
[Other] Bhattacharyya DL : 0: 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
Update : 2025-04-26 Size : 3.45mb Publisher : vidi

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