基于遗传算法的海底沉积物纹理特征优化方法
文献类型：期刊论文
| 作者 | 李文莉 ; 高宏伟; 冀大雄; 李岩
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| 刊名 | 计算机科学
 |
| 出版日期 | 2016 |
| 卷号 | 43期号:S1页码:130-133,141 |
| 关键词 | 海底沉积物 遗传算法 纹理特征分析 灰度共生矩阵 分形理论 自组织映射神经网络 |
| ISSN号 | 1002-137X |
| 其他题名 | Optimization Method of Seabed Sediment Texture Feature Based on Genetic Algorithm |
| 产权排序 | 1 |
| 通讯作者 | 李文莉 |
| 中文摘要 | 为了提高水下机器人对海底沉积物的自主分类感知能力,解决特征冗余问题,对利用遗传算法优化海底沉积物纹理特征进行了研究。以基于灰度共生矩阵和分形理论提取多种海底沉积物视觉纹理特征实现海底沉积物分类识别为背景,提出利用遗传算法对纹理特征项进行优化选择以实现对提取特征的降维,并将降维后的特征项作为自组织映射神经网络模型的输入,对海底沉积物进行视觉分类,提高水下机器人作业时的环境感知能力。实验结果表明,相对于未优化的纹理特征,优化后的纹理特征在海底沉积物分类识别中具有更优的分类效果。 |
| 英文摘要 | In order to improve autonomous sensing perception of underwater vehicle on classification of seabed sediments and solve the problem of features redundancy, using genetic algorithm to optimize texture features of seabed sediments was studied. In the background of the classification and identification of seabed sediment based on a variety of seabed sediment visual texture features that are extracted based on gray level co-occurrence matrix and fractal theory , the reduction of feature dimension has been realized by using the genetic algorithm to optimize the texture features, and the texture features after dimension reduction are trained by a self - organizing mapping neural network as inputs for visual classification of seabed sediments, improving the environmental awareness of underwater vehicle on underwater operation. The experimental results show that with respect to the texture features that are not optimized , optimized texture features have better classification effect in seabed sediment classification and recognition. |
| 语种 | 中文 |
| 源URL | [http://ir.sia.cn/handle/173321/18798]  |
| 专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
| 推荐引用方式 GB/T 7714 | 李文莉,高宏伟,冀大雄,等. 基于遗传算法的海底沉积物纹理特征优化方法[J]. 计算机科学,2016,43(S1):130-133,141. |
| APA | 李文莉,高宏伟,冀大雄,&李岩.(2016).基于遗传算法的海底沉积物纹理特征优化方法.计算机科学,43(S1),130-133,141. |
| MLA | 李文莉,et al."基于遗传算法的海底沉积物纹理特征优化方法".计算机科学 43.S1(2016):130-133,141. |
入库方式： OAI收割
来源：沈阳自动化研究所
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