Reservoir Computing based Neural Image Filters

被引：0

作者：

Ganguly, Samiran ^{[1
]}

Gu, Yunfei ^{[1
]}

Xie, Yunkun ^{[1
]}

Stan, Mircea R. ^{[1
]}

Ghosh, Avik W. ^{[1
]}

Dhar, Nibir K. ^{[2
]}

机构：

[1] Univ Virginia, Charles L Brown Dept Elect & Comp Engn, Charlottesville, VA 22904 USA

[2] US Army, Night Vis & Elect Sensors Directorate, Ft Belvoir, VA 22060 USA

来源：

IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2018年

关键词：

Computer Vision; Machine Vision; Reservoir Computing; Echo-State Networks; Neuro-Adaptive Filtering;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Clean images are an important requirement for machine vision systems to recognize visual features correctly. However, the environment, optics, electronics of the physical imaging systems can introduce extreme distortions and noise in the acquired images. In this work, we explore the use of reservoir computing, a dynamical neural network model inspired from biological systems, in creating dynamic image filtering systems that extracts signal from noise using inverse modeling. We discuss the possibility of implementing these networks in hardware close to the sensors.

引用

页码：3206 / 3210

页数：5

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