离散过程神经网络和CGA相融合的边缘检测  

Edge Detection Based on Discrete Process Neural Networks and CGA Fusion Algorithm

作　　者：张宇航[1] 许少华[1,2] 许辰[1] 

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]山东科技大学信息科学与工程学院,山东青岛266590

出　　处：《计算技术与自动化》2016年第4期11-14,共4页Computing Technology and Automation

基　　金：国家自然科学基金资助项目(61170132)

摘　　要：针对图像处理中的边缘检测问题,提出一种基于离散过程神经网络(DNPP)与混沌遗传算法(CGA)相融合的模型和算法。DNPP的输入可以直接为数据矩阵,实现二维图像关联信息网格化的整体输入和处理。以图像网格灰度值作为DPNN处理数据集合,利用Sobel算子检测的技术原理和DPNN的判别能力,实现图像边界线的自动辨识和追踪。文中给出基于混沌遗传搜索的DNPP求解算法,以油田地震数据体切片中砂体的识别和追踪为例,实际资料处理结果验证了检测模型和算法的有效性。In view of the edge detection problems in image processing, this paper proposed a detection model and algo- rithm based on the combination of discrete process neural network （DPNN） and chaotic genetic algorithm（CGA） . Inputting DNPP can be the data matrix, which can realize the goal of inputting as well as processing the two-dimensional image corre- lation gridding information integrally. Putting image grey value as DPNN processing data collection and using the technology principle of Sobel operator detection as well as the recognition ability of DPNN, it can reach the goal that the image border line can identify and track automatically. Taking the identification and tracking of the sand body in the oil field seismic data for example, the results show the DNPP algorithm based on the chaos genetic search is effective in detecting model and algo- rithm.

关 键 词：图像处理 边缘检测 离散过程神经网络 混沌遗传算法 砂体识别和追踪 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]