计算机科学
模式识别(心理学)
人工智能
特征(语言学)
卷积神经网络
特征提取
保险丝(电气)
GSM演进的增强数据速率
边缘检测
融合
图像处理
图像(数学)
工程类
哲学
语言学
电气工程
作者
Ronghao Xian,Xin Xiong,Hong Peng,Jun Wang,Antonio Ramírez-de-Arellano,Qian Yang
标识
DOI:10.1016/j.patcog.2023.110112
摘要
NSNP-type neuron is a new type of neuron model inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In order to address the loss problem of edge detail information in edge detection methods based on deep learning, we propose a feature fusion method based on NSNP-type neurons. The architecture of this feature fusion method consists of two modules: feature extraction module and feature fusion module. In particular, the feature fusion module is composed of convolutional blocks constructed by NSNP-type neurons for multi-level feature fusions, and CoT blocks with Transformer style is introduced to extract rich contextual information from low-level features and high-level features. To fuse multi-level features and preserve contextual information, we design a new loss function that not only preserves feature prediction loss and fusion loss, but also considers contour-related and texture-related information. The proposed method is evaluated on BSDS500 and NYUDv2 data sets and compare it with 9 baseline methods and 12 CNN-based methods, and we achieve ODS of 0.808 and OIS of 0.827 on BSDS500. The comparison results demonstrate the advantages of the proposed method for edge detection. The source code is available at https://github.com/xhuph66/FF-CNSNP-master.
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