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| 增强现实系统中磁力跟踪器的校正 | |
| 引用本文： | 丰艳,王明辉,陈一民,韩进. 增强现实系统中磁力跟踪器的校正[J]. 计算机工程, 2006, 32(10): 28-30 |
| 作者姓名： | 丰艳 王明辉 陈一民 韩进 |
| 作者单位： | 1. 上海大学计算机工程与科学学院,上海,200072 2. 曲阜师范大学数学科学学院,曲阜,273165 3. 上海大学计算机工程与科学学院,上海,200072;山东科技大学信息科学与工程学院,泰安,271019 |
| 摘 要： | 提出了利用BP神经网络对跟踪器进行校正。针对神经网络训练速度慢、容易陷入局部极值的情况，首先利用具有良好全局搜索能力的遗传算法来优化BP神经网络的各层初始权值和阈值，为后续神经网络的搜索定位出一个优化的搜索空间。实验结果表明，利用该遗传神经网络方法进行跟踪器校正，能够显著提高增强现实系统的精度，有助于提高增强现实系统的真实感。 |
| 关 键 词： | BP神经网络 遗传算法 增强现实 磁力跟踪器 校正 |
| 文章编号： | 1000-3428（2006）10-0028-03 |
| 收稿时间： | 2005-07-06 |
| 修稿时间： | 2005-07-06 |
Rectification of Magnetic Force Tracker in AR System |
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| FENG Yan,WANG Minghui,CHEN Yimin,HAN Jin. Rectification of Magnetic Force Tracker in AR System[J]. Computer Engineering, 2006, 32(10): 28-30 | |
| Authors: | FENG Yan WANG Minghui CHEN Yimin HAN Jin |
| Affiliation: | 1. School of Computer Engineering and Science, Shanghai University, Shanghai 200072; 2. School of Mathematics and Science, Qufu Normal University, Qufu 273165; 3. Information Science and Engineering College, Shandong University of Science and Technology, Taian 271019 |
| Abstract: | An approach using BP neural network is proposed to rectify the error. Considering that the neural network is prone to get local extremum and its convergence speed is slow, it utilizes the excellent global searching ability of genetic algorithm to optimize the initial weights and threshold of neural network. The results of experiment show that this method can not only improve the convergence precision of weights but also insure the neural network to get global convergence fleetly. After the tracker is rectified with the above method, the precision of AR system is improved prominently, consequently to enhance the third dimension of AR system. |
| Keywords: | BP neural network Genetic algorithm Augmented reality Magnetic force tracker Rectification |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |