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Object recognition using quantum holography with neural-net preprocessing

机译:使用具有神经网络预处理的量子全息术进行对象识别

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摘要

It is computationally demonstrated how quantum associative networks, implemented using quantum holography, Could be harnessed for object recognition. These Simulated quantum nets alone execute efficient image recognition, i.e., reconstruction of an image selected from associative memory (hologram). However, optically implementable neural-net preprocessing of object-images is needed for appearance-based viewpoint-invariant recognition of objects. We present computer simulation results of two methods: Moore-Penrose orthogonalization and encoding of object-images with Gabor wavelets. A computer-supported quantum Gabor-wavelet holography is proposed. (c) 2005 Optical Society of America.
机译:它通过计算演示了如何使用量子全息术实现的量子关联网络进行对象识别。这些模拟量子网络单独执行有效的图像识别,即重建从联想记忆(全息图)中选择的图像。然而,对于基于外观的视点不变识别对象,需要对对象图像进行光学可实现的神经网络预处理。我们给出了两种方法的计算机模拟结果:Moore-Penrose正交化和Gabor小波对物体图像进行编码。提出了一种计算机支持的量子Gabor小波全息术。(c) 2005年美国光学学会。

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