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Study to real-time target recognizing system based on optical correlator

机译:基于光学相关器的实时目标识别系统研究

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The recognition algorithm based on geometry texture character includes pattern matching, estimation function, and determination tree. The advantage of above algorithms is easy to be programmed and realized by computer. However, the recognizing operation consumes the mass of system source, which lead to the difficult to assure the good recognizing accuracy under the high recognizing speed. Optical correlation processing system transmits information by photons and operates by photo-electronic device, and realizes the target recognition by optical refraction, interference, and diffraction. Compared with traditional computer image processing techniques, optical correlation system offer primarily two types of benefits. On the one hand, optical correlation systems have an inherent capability for parallel processing, which can transmit rapidly mass information. On the other hand, it changes the complex images processing problem into spot recognizing in output plane. The correlation peak of high gray value appeared in the correlation plane can be used to the proof to tell target from many kinds noises. The analysis in paper to how to make the complex matching filter, how to determinate the truly correlation peak from others signal in correlation plane rely on neural nets technology could be helpful to the study of optical correlation system used to the field of image recognition.
机译:基于几何纹理字符的识别算法包括模式匹配,估计函数和确定树。易于编程和通过计算机实现上述算法的优点。然而,识别操作消耗了系统源的质量,这导致难以确保在高识别速度下的良好识别精度。光学相关处理系统通过光子发送信息并由光电装置操作,并通过光学折射,干扰和衍射实现目标识别。与传统的计算机图像处理技术相比,光学相关系统主要提供两种类型的益处。一方面,光学相关系统具有用于并行处理的固有能力,其可以传输快速质量信息。另一方面,它将复杂的图像处理问题改变为输出平面中的点识别。相关平面中出现的高灰度值的相关峰可以用于识别来自许多噪声的目标。涉及如何制作复杂匹配过滤器的纸张分析,如何确定来自相关平面中的其他信号的真正相关峰值依赖于神经网络技术可以有助于研究用于图像识别领域的光学相关系统。

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