RUI: Fractional Power Fringe - Adjusted Filter Based Joint Transform Correlation

提案编号：ECS-9705668 主要研究者：Mohammad Alam 研究在本科院校（RUI）：分数功率条纹调整的联合变换相关器 在本研究中，我们将探讨一种新的实时失真不变滤波器程序，以应用于光学模式识别。 光学模式识别涉及使用基于匹配滤波器的相关器或联合变换相关器（JTC）;只有后者适合于实时应用。 近年来，人们提出了许多用于光学模式识别的非线性光学技术，而全光折变晶体JTC就是其中的佼佼者。 目前，光折变JTC结构仅适用于单目标检测，且处理速度较慢。 PI的研究将开发一种新的广义JTC，它采用非线性切趾在傅立叶平面。 PI的初步研究表明，与其他JTC架构相比，这种JTC技术可以产生更好的相关输出。 这似乎适用于多个目标，这可能是相对于彼此旋转，以及作为单一的目标;和噪声以及无噪声的背景。这种改进的光学模式识别技术有许多应用：用于工业自动化的机器部件识别和标签检查、光学字符识别、超快指纹识别、目标检测和跟踪、机器人视觉、自动化智能车辆控制系统，以及在广域搜索情况下的实时感兴趣区域识别器，例如特定特征的识别，地球资源图像中的模式或特征。 PI将探索各种架构，通过广泛的建模，仿真和实验，有效地实现非线性傅立叶域处理。 实验将与代顿大学的电光实验室合作进行。计划在INPU-韦恩和代顿大学让所有本科生参与这项研究。

Proposal Number: ECS-9705668 Principal Investigator: Mohammad Alam Title: Research in Undergraduate Institutions (RUI): Fractional Power Fringe-Adjusted Based Joint Transform Correlator In this research, a novel real-time distortion-invariant filter procedure for optical pattern recognition will be investigated. Optical pattern recognition involves the use of either a matched filter based correlator or a joint transform correlator (JTC); only the later is suitable for real-time applications. Recently a number of nonlinear optical techniques have been proposed for optical pattern recognition, and the all-optical photorefractive crystal JTC stands out among these. To date, the photorefractive JTC architecture is suitable only for single target detection and the processing speed is slow. The PI's research will develop a novel generalized JTC which employs nonlinear apodization at the Fourier plane. Preliminary studies by the PI indicate that this JTC technique results in better correlation output compared to alternate JTC architectures. This seems true for multiple targets, which may be rotated with respect to each other and well as single targets; and for noisy as well as noise-free backgrounds. There are many applications for such an improved optical pattern recognition technique: machine parts recognition and label inspection for industrial automation, optical character recognition, ultrafast fingerprint identification, target detection and tracking, robotics vision, automated intelligent vehicle control systems, and real-time region-of-interest identifier in broad area search situations such as the identification of specific features, patterns or characteristics in earth resource images. The PI will explore various architectures to effectively implement the nonlinear Fourier domain processing, through extensive modeling, simulation and experimentation. The experimentation will be carried out in collaboration with the Electro-Optics Laboratory at University of Dayton. Fu ll undergraduate student participation in the research at INPU-Fort Wayne and at University of Dayton is planned.