Open AccessButton recognition with texture feature based on spiking neural network
Author(s) -
Zhang Zhenmin,
Wu Qingxiang,
Lai Xiaoyan,
Lin Xiufang
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8283
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , feature extraction , artificial neural network , feature (linguistics) , texture (cosmology) , segmentation , image (mathematics) , spiking neural network , image texture , computer vision , image segmentation , philosophy , linguistics
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.