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Feature Extraction and Image Recognition with Convolutional Neural Networks

机译:卷积神经网络的特征提取和图像识别

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The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.
机译:人类具有非常复杂的感知系统,包括视觉,听觉,嗅觉,触摸和烧伤。本文将介绍最近关于为图像识别提供技术解决方案的研究,通过应用于动物视觉系统启发的算法,该算法应用于卷积神经网络(CNN)。卷积作为视神经电池的完美实现,其仅响应其接收领域,并且在图像特征提取中表现良好。作为高度分层网络,CNN由一系列不同的功能层构成。本文用各层分离并清楚地描述功能块。另外,介绍了Mnist数据库上的识别过程和先驱CNN的结果。

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