提出了一种以人的动作序列图像的轮廓为特征，基于RBF神经网络的日常行为识别方法。
RBF neural network-based method for action recognition is presented by using the contours of image sequence as representative descriptors of human posture to achieve daily human actions recognition.
提出了一种以人的动作序列图像的轮廓为特征，基于RBF神经网络的日常行为识别方法。
A RBF neural network-based method for action recognition is presented by using the contours of image sequence as representative descriptors of human posture to achieve daily human actions recognition.
对运动人手序列图像的对比跟踪实验表明，这种融合使得可见光图像中动态轮廓线平均跟踪误差减小了60.25%。
A contrasting experiment on moving hand image sequence indicates average tracking error of dynamic contour has decreased by 60.25% in visible image with this image fusion.
并采用边缘跟踪算法，获取运动物体的外部轮廓特征，对序列图像进行处理，继而实现对运动物体进行识别跟踪。
Then, adopt the edge tracking algorithm to acquire the exterior outline feature of moving object and process the sequence images. After that, the recognized tracking is carried out for moving object.
在得到人体实体切片序列图像中大脑器官的边缘轮廓线的基础上，提取图像中的标尺，利用图像中提供的标尺计算像素和实物的比例关系；
On the base of distilling edges from images of human slices, by calibrating the gauge, the ratio relationship between the pixels of the gauge and the real section areas were obtained.
在得到人体实体切片序列图像中大脑器官的边缘轮廓线的基础上，提取图像中的标尺，利用图像中提供的标尺计算像素和实物的比例关系；
On the base of distilling edges from images of human slices, by calibrating the gauge, the ratio relationship between the pixels of the gauge and the real section areas were obtained.
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