离散过程神经网络和CGA相融合的边缘检测

张宇航; 许少华; 许辰

离散过程神经网络和CGA相融合的边缘检测

引用本文：张宇航, 许少华,许辰.离散过程神经网络和CGA相融合的边缘检测[J].计算技术与自动化,2016,(4):11-14

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作者	单位
张宇航, 许少华,许辰	(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)

中文摘要: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。

中文关键词: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

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

Abstract:In view of the edge detection problems in image processing, this paper proposed a detection model and algorithm 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 correlation 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 algorithm.

keywords:image processing edge detection discrete process neural networks chaos genetic algorithm sand body tracking and identification

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