基于多通道联合变换相关器的旋转和缩放不变性目标识别方法  

Distortion-Invariant Target Recognition Based on Multichannel Joint Transform Correlator

作　　者：林超 韩艳丽 娄树理 刘珮 张文龙[3] 杨自康 Lin Chao;Han Yanli;Lou Shuli;Liu Pei;Zhang Wenlong;Yang Zikang(School of Aviation of Operations and Support,Naval Aviation University,Yantai 264000,Shandong,China;School of Opto-Electronic Information Science and Technology,Yantai University,Yantai 264000,Shandong,China;Unit 92485 of PLA,Dalian 116041,Liaoning,China)

机构地区：[1]海军航空大学航空作战勤务学院,山东烟台264000 [2]烟台大学光电信息科学与工程学院,山东烟台264000 [3]中国人民解放军92485部队,辽宁大连116041

出　　处：《中国激光》2022年第13期91-106,共16页Chinese Journal of Lasers

基　　金：国家自然科学基金(62005318)。

摘　　要：为充分利用光学相关识别系统的空间-频谱带宽,提高光学相关识别系统的速度和精度,提出一种基于多通道联合变换相关器和综合鉴别函数的旋转和缩放不变性目标识别方法,并将峰值位置变化标准差作为识别判据。首先,分析以局域峰值杂波均值为相位优选约束条件时存在的问题,提出新的相位优选约束条件——峰值位置变化值,用于选择一定旋转和缩放范围内目标的公共相位,将公共优选相位用于多通道联合变换相关器并结合综合鉴别函数实现了旋转和缩放不变性识别;然后,分析了当目标缩小或综合鉴别函数集成训练图像数量增加时系统的识别容限;最后,分析了场景图像中背景发生变化时所提方法的适用性。研究结果表明,在设定的图像尺寸和背景复杂度条件下,所提方法能够在9通道并行处理的前提下对目标缩小60%、综合鉴别函数集成9幅图像的旋转和缩放目标进行有效识别,且对像素数变化不超过50%的背景具有较好的适应性,提高了目标识别的速度和精度,对多通道光学相关器的实用化具有重要意义。Objective With the advent of the big data and intelligence eras,information systems require considerably enhanced performance and low energy costs.Optical computing may become the next-generation computing platform owing to its parallel processing capability and high bandwidth with low energy consumption.In pattern recognition applications,large amounts of image data must be rapidly processed.Two types of optical approaches have been investigated for pattern recognition:optical neural network,which comprises two subclasses including silicon photonic-based neural networks,and free-space-based optical network.The former has undergone considerable advancements recently owing to improved fabrication capability and novel network components based on optics such as microring resonators and Mach-Zehnder interferometers.The latter(e.g.,diffractive neural networks)is also important,particularly for computational imagingbased applications.However,optical neural network-based pattern recognition approaches are immature owing to the implementation of nonlinear functions.Pattern recognition approaches founded on free-space-based optical networks are hybrid optoelectronic correlators,far more mature than optical neural network-based ones.The correlator can be codesigned with a neural network to serve as a coprocesser to prefilter some image features for ultrafast processing.However,in conventional optical correlators,both the spatial and spectral bandwidths of systems have not been efficiently used when performing the correlation operation.Hence,the inherent parallel processing capability of optics cannot be fully exploited.Methods In our previous work,a multichannel joint transform correlation method is proposed based on the compression and translation of joint transform power spectrum to fully utilize spatial and spectral bandwidths and enhance the parallel processing efficiency and recognition accuracy of optical correlation systems.In the input plane of this scheme,the scene image and N numbers of reference images are uploa

关 键 词：信息处理 光学模式识别 多通道联合变换相关器 综合鉴别函数 旋转和缩放不变性识别 

分 类 号：O438[机械工程—光学工程] TP751[理学—光学]