Button recognition with texture feature based on spiking neural network

The Journal of Engineering

Zhenmin Zhang¹  Xiufang Lin²  Xiaoyan Lai³  Qingxiang Wu⁴ 

[1] Electronics and Information Science College of Fujian Jiangxia University , Fuzhou , People'Jinshan College of Fujian Agriculture and Forestry University , Fuzhou , People'Photonic and Electronic Engineering College of Fujian Normal University , No. 8 , Shangsan Road , Cangshan District , Fuzhou , People's Republic of China

关键词: buttons;    button image feature extraction;    image classification;    analogous textures;    texture segmentation;    spiking neural network;    image recognition;    image structure characterisation analysis;    button images;    nonlinear information processing;    co-occurrence matrix algorithm;    button recognition;    texture feature extraction;    unsupervised feature extraction;    visual images;    supervised feature extraction;    neuron network;    neural networks;   

DOI  :  10.1049/joe.2018.8283

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

Spiking neuron network is generally considered as the third generation of neural networks. This type of network is a class of machine learning techniques that exploit many layers of non-linear information processing for supervised or unsupervised feature extraction, image classification, texture segmentation, and image recognition. On the other hand, the grey-level co-occurrence matrix algorithm is widely used in visual images for texture feature extraction and image structure characterisation analysis. For those buttons with the same size, same shape, similar colours, and analogous textures, they cannot be effectively identified by conventional methods. At this time, the spiking neural network trained with the improved GLCM algorithm can be used to achieve button image feature extraction, classification, and recognition. Experiments show that the method proposed here can effectively segment the button images with their texture features.

【 授权许可】

CC BY