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	Multimodal Unknown Surface Material Classification and Its Application to Physical Reasoning 期刊论文 OAI收割 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 2022, 卷号: 18, 18, 期号: 7, 页码: 4406-4416, 4406-4416 作者: Wei, Junhang; Cui, Shaowei; Hu, Jingyi; Hao, Peng; Wang, Shuo \| 收藏 \| 浏览/下载：48/0 \| 提交时间：2022/06/10 Robots Robots Convolutional neural networks Visualization Informatics Feature extraction Task analysis Haptic interfaces Auditory and haptic information deep learning multimodal fusion physical reasoning unknown surface material classification (USMC) Convolutional neural networks Visualization Informatics Feature extraction Task analysis Haptic interfaces Auditory and haptic information deep learning multimodal fusion physical reasoning unknown surface material classification (USMC)
	图像目标检测与识别技术研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2010 作者: 夏晓珍收藏 \| 浏览/下载：225/0 \| 提交时间：2015/09/02 目标检测与识别分类识别图像检索局部特征目标匹配部件结构级联分类器多信息融合语义上下文广告分类 object detection and recognition category recognition image retrieval local feature object matching part-based structure cascade classifier multi-information fusion semantic context ads classification
	基于支持向量机遥感图像融合分类方法研究进展期刊论文 OAI收割安徽农业科学, 2010, 卷号: 38, 期号: 17, 页码: 9235-9238 作者: 郭立萍; 唐家奎; 米素娟; 张成雯; 赵理君 \| 收藏 \| 浏览/下载：2/0 \| 提交时间：2024/05/07 Remote sensing image Information abstraction Fusion and classification Support vector machine 遥感图像信息提取融合分类支持向量机
	Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文 OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者: Wang D.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 In feature-level fusion recognition system the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general there are two main missions. One is improving the recognition correct rate as soon as possible the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions this paper presents a more rational and accurate optimization Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.