基于均值位移算法的体育运动训练轨迹数据捕捉方法

为解决背景复杂、捕捉目标运动速度较快导致的轨迹获得不完全等问题,提出基于均值位移算法的体育运动训练轨迹数据捕捉方法。将人体模型视为具有51个自由度及16个关节的骨架模型实现训练轨迹的数据化,为减少计算量对轨迹数据做降维处理。为降低均值位移算法对环境参数的依赖性,选择梯度迭代估计算法中的概率密度函数,并将目标的颜色信息作为特征完成轨迹数据捕捉。实验表明方法能够捕捉到运动员各个关节的运动情况,可以在不依赖相关参数的条件下更精准地完成训练轨迹数据的捕捉工作。

Moving Target Tracking Based on LogGabor Wavelet and Mean-shift Algorithm

Tracking moving target from image or video sequence is a hot research topic in computer vision. An algorithm based on LogGabor wavelet and Mean-shift has been proposed for moving target tracking under fixed camera setting and complicated environment. Phase coherency of LogGabor wavelet facilitates to extract the edge of moving target and check noise. According to the edge detection,the starting location of Mean-shift can be estimated using the target center coordinate. Eventually,a real-time moving target can be extracted by doing iterative matching pursuit,and experimental results proved the effectiveness of the method proposed.

Mean-shift滤波和直方图增强的图像弱边缘提取

准确定位大脑中的功能区域需要我们对fMRI图像中头骨等生物组织精确提取,但由于核磁共振仪等现有条件的限制,头骨等弱边缘信息难于提取。针对这一问题,提出一种弱边缘提取的有效方法。首先利用Mean-shift聚类特性,在不损失目标信息的情况下,削弱图像中的噪声干扰;根据滤波后像素灰度的区域分布特性,对头骨区域进行直方图增强,最后应用边缘检测算法将头骨边缘提取出来。实验的结果表明,该方法可以有效地将fMRI图像中的头骨弱边缘信息提取出来。