Neuron-based VLSI architecture for real-time camera distortion correction

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107097

Title:	Neuron-based VLSI architecture for real-time camera distortion correction
Authors:	陳慶瀚;Chen, C-H;Yao, T-K
Contributors:	資訊電機學院資訊工程學系
Keywords:	Accuracy;Back propagation;Calibration;Cameras;Field programmable gate arrays;Manufacturing;Mathematical models;Methods;Neural networks;Neurons;Propagation;Read only memory;Real time;ROM;Software;Studies
Date:	2015-01-01
Issue Date:	2026-04-23 13:56:12 (UTC+8)
Publisher:	Sharif University of Technology;Tehran: Sharif University of Technology
Abstract:	摘要： This article proposes the efficient VLSI architecture of camera distortion correction, based on a Neural Camera Distortion Model (NCDM). Conventional imaging methods use over two kinds of models to correct the camera and lens distortions, but the NCDM uses a single model to immediately correct the geometry distortion and unsymmetrical manufacturing errors. The NCDM, with four neurons, performs a wide-angle distortion correction. The results show that the maximal corrected error in a whole image is less than 1.1705 pixels, and the MSE approaches 0.1743 between corrected and ideal results. The distortion correction by NCDM is 429x more accurate than the conventional approach. The chip size of NCDM is 1:51 x 1:51 mm2 and contains 126 K gates, using the TSMC 90 nm CMOS technology process. Working at 240 Mhz, this architecture can correct 30 frames and a Full-HD resolution video per second. Results show that the maximal corrected error in a whole image is less than 1.4 pixels, and the mean square error approaches 0.0376 between corrected and ideal results. 出版者： Tehran: Sharif University of Technology 出版日期： 2015-12-01 出處： Scientia Iranica. Transaction B, Mechanical engineering, 2015-12, Vol.22 (6), p.2150 版權： Copyright Sharif University of Technology 2015
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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