计算机科学与探索
計算機科學與探索
계산궤과학여탐색
JOURNAL OF FRONTIERS OF COMPUTER SCIENCE & TECHNOLOGY
2014年
10期
1254-1262
,共9页
粒子群遗传算法%摄像机标定%BP神经网络
粒子群遺傳算法%攝像機標定%BP神經網絡
입자군유전산법%섭상궤표정%BP신경망락
particle swarm optimization genetic algorithm%camera calibration%BP neural network
摄像机标定是从二维图像提取三维空间信息的关键步骤,标定的精度直接关系到三维重构结果的逼真程度。为了有效解决传统摄像机标定算法中的多参数、计算费时费力等问题,提高摄像机标定的精度和速度,将粒子群遗传算法(particle swarm optimization genetic algorithm,PSO-GA)应用于摄像机标定中。对参数进行粒子群算法优化后,再使用遗传算法中的选择、交叉和变异等操作进行参数优化,以实现粒子群算法与遗传算法的融合。结合后的算法全局搜索能力较强,收敛速度更快,优化能力与鲁棒性得以提高。同时,基于神经网络的摄像机标定方法所能覆盖的标定空间十分有限,提出了一种采用粒子群遗传算法优化BP神经网络的摄像机标定方法,以解决传统摄像机标定方法难以解决的问题。实验数据表明,基于粒子群遗传算法的BP神经网络标定是一种可行的方法,标定精度高,收敛速度快,泛化能力强。
攝像機標定是從二維圖像提取三維空間信息的關鍵步驟,標定的精度直接關繫到三維重構結果的逼真程度。為瞭有效解決傳統攝像機標定算法中的多參數、計算費時費力等問題,提高攝像機標定的精度和速度,將粒子群遺傳算法(particle swarm optimization genetic algorithm,PSO-GA)應用于攝像機標定中。對參數進行粒子群算法優化後,再使用遺傳算法中的選擇、交扠和變異等操作進行參數優化,以實現粒子群算法與遺傳算法的融閤。結閤後的算法全跼搜索能力較彊,收斂速度更快,優化能力與魯棒性得以提高。同時,基于神經網絡的攝像機標定方法所能覆蓋的標定空間十分有限,提齣瞭一種採用粒子群遺傳算法優化BP神經網絡的攝像機標定方法,以解決傳統攝像機標定方法難以解決的問題。實驗數據錶明,基于粒子群遺傳算法的BP神經網絡標定是一種可行的方法,標定精度高,收斂速度快,汎化能力彊。
섭상궤표정시종이유도상제취삼유공간신식적관건보취,표정적정도직접관계도삼유중구결과적핍진정도。위료유효해결전통섭상궤표정산법중적다삼수、계산비시비력등문제,제고섭상궤표정적정도화속도,장입자군유전산법(particle swarm optimization genetic algorithm,PSO-GA)응용우섭상궤표정중。대삼수진행입자군산법우화후,재사용유전산법중적선택、교차화변이등조작진행삼수우화,이실현입자군산법여유전산법적융합。결합후적산법전국수색능력교강,수렴속도경쾌,우화능력여로봉성득이제고。동시,기우신경망락적섭상궤표정방법소능복개적표정공간십분유한,제출료일충채용입자군유전산법우화BP신경망락적섭상궤표정방법,이해결전통섭상궤표정방법난이해결적문제。실험수거표명,기우입자군유전산법적BP신경망락표정시일충가행적방법,표정정도고,수렴속도쾌,범화능력강。
Camera calibration is a key step for extracting three-dimensional information from two-dimensional image, which directly determines the accuracy of 3D reconstruction. In order to solve the problem of multiple parameters, reduce the computational cost, promote the accuracy and speed of camera calibration, this paper firstly applies particle swarm optimization genetic algorithm (PSO-GA) to camera calibration. The initial parameters of the genetic algo-rithm are optimized by particle swarm optimization. After that, the parameters are optimized by the selection, cross-over and mutation operations of genetic algorithm, which can realize the integration of particle swarm optimization and genetic algorithm. The resulting algorithm has stronger global search ability, faster convergence speed, better optimization ability and robustness. At the same time, the camera calibration method based on neural network just can cover very limited calibration space, this paper proposes a new camera calibration method using particle swarm optimization genetic algorithm to optimize the BP neural network, in order to solve the problem that the traditional camera calibration method is difficult to solve. The experimental data show that the BP neural network calibration based on particle swarm optimization genetic algorithm is a feasible method, which has high calibration precision, fast convergence speed and strong generalization ability.