远程采集图像特征的优化识别过程仿真

Simulation of Optimization and Recognition of Image Features in Remote Collection

提出一种基于轮廓线曲线分割的远程采集图像特征的优化识别算法。该算法采用能量最小化约束进行图像滤波,使得滤波器能在满足约束条件下输出的能量最小,将图像的边缘两相流分组成两部分子集,构造出基于能量最小化约束的目标降噪和边缘特征检测模型,基于轮廓线曲线分割理论,得到远程采集图像的轮廓线曲线分割边缘函数演化方程,提取到远程采集图像的空间信息、视觉信息和结构信息特征,实现远程采集图像特征的优化识别。仿真结果表明,该算法能有效抑制背景噪声,能突出远程小目标图像结构特征,提高信噪比,并且实现了对远程采集图像特征的优化识别,图像特征识别效果较好。

An optimization algorithm of remote image acquisition is proposed based on contour curve segmentation in this paper. The algorithm uses energy minimization constraints for image filtering, so that the filter can meet the constraints on the minimum output energy. The two-phase flow edge image is composed of two subsets, and the target noise reduction and edge feature detection model is constructed based on energy minimization constraint. Based on contour curve theory, the remote acquisition of image segmentation edge function evolution equation is got to extract remote image acquisition of spatial information and visual information and the structure information characteristics, realizing the optimization of the remote acquisition of image feature recognition. Simulation results show that the algorithm can effectively restrain the background noise, highlight the structural characteristics of remote small target image, and improve the signal to noise ratio. The remote acquisition of image feature information extraction is accurate and comprehensive, it can realize the optimization of recognition of remote collection of image features, and the image characteristic has good recognition effect.