运动视频图像边缘特征自适应识别仿真

Edge Recognition Adaptive Recognition Simulation of Motion Video Images

运动视频图像边缘特征的自适应识别,在提高图像利用率方面的意义重大。在对运动视频图像边缘特征进行识别时,需对图像的灰度值进行计算,确定视频图像的离散系数。传统方法主要根据图像的特征向量进行分析,导致出现图像特征识别效果差和识别错误率高的问题,提出基于赋时矩阵的运动视频图像特征识别方法。根据运动视频图像的RGB颜色空间,计算视频图像平均矢量值和矢量距离,利用图像的特征向量和权向量分析运动视频图像的线性函数,并统计运动视频图像的类别,根据运动视频图像的决策规则来对图像进行决策分类和分割,在此基础上引入图像的局部离散系数,得到运动视频图像的离散矩阵,计算图像的灰度值和动态阈值,并对图像边缘特征进行归一化处理,完成对运动视频图像局部边缘特征的自适应识别。仿真结果表明,提出方法对运动视频图像边缘特征的识别效果好,并且图像特征识别的错误率低。

The self-adaptive recognition for edge feature of motion video image is very important on improving the image utilization. The traditional method ignores the problem of poor effect of image feature recognition and high recognition error rate. Therefore, a method to recognize the motion image feature based on timed matrix was proposed. According to RGB color space of motion video image, the average vector value and vector distance of video image was calculated. Then, feature vector and weight vector of image was used to analyze the linear function of motion video image. Meanwhile, the categories of motion video image were counted. According to the decision rule of motion video image, decision, classification and segmentation were performed on the image. On this basis, the local discrete coefficient of image was introduced to obtain the discrete matrix of motion video image. Finally, the gray value and dynamic threshold of image was calculated, and the image edge feature was normalized. Thus, we completed self-adaptive recognition for local edge feature of motion video image. Simulation results prove that the proposed method has good recognition effect on the edge feature of motion video image. Meanwhile, the error rate of image feature recognition is low.