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	A Micro-Multispectral Vision Sensor: Research on on-line measurement classification and recognition method of coal gangue 会议论文 OAI收割 Beijing, China, 2023-07-25 作者: Guo, Quan; Liu, Ruqi; Wu, Dengshan; Yu, Weixing \| 收藏 \| 浏览/下载：13/0 \| 提交时间：2024/02/07 Micro-Multispectral Vision Sensor SVM Classification and Recognition Coal Gangue
	面向自动化学科中文期刊论文的文本挖掘系统学位论文 OAI收割工程硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2012 作者: 刘禹收藏 \| 浏览/下载：73/0 \| 提交时间：2015/09/02 文本挖掘文本分类文本聚类命名实体识别与消歧知识服务 text mining text classification text clustering name entity recognition and disambiguation knowledge service
	基于机器视觉的钕铁硼表面缺陷检测系统学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2012 吴亮收藏 \| 浏览/下载：163/0 \| 提交时间：2015/09/02 目标分割特征提取分类识别凸缺陷分析图像细化 Object Segmentation Feature Extraction Classification and Recognition Convexity Defects Analysis Image Thinning
	On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE) 会议论文 OAI收割 2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China 作者: Yu P.; Yu P.; Gao X. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 As an effective statistical learning tool AdaBoosting has been widely used in the field of pattern recognition. In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality whilst to remove noise bands simultaneously. Then we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
	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：224/0 \| 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
	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.
	Infrared face recognition using linear subspace analysis (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral Image Processing and Pattern Recognition, October 30, 2009 - November 1, 2009, Yichang, China 作者: Wang D. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 Infrared image offers the main advantage over visible image of being invariant to illumination changes for face recognition. In this paper based on the introduction of main methods of linear subspace analysis such as Principal Component Analysis (PCA) Linear Discriminant Analysis(LDA) and Fast Independent Component Analysis (FastICA) the application of these methods to the recognition of infrared face images offered by OTCBVS workshop are investigated and the advantages and disadvantages are compared. Experimental results show that the combination approach of PCA and LDA leads to better classification performance than single PCA approach or LDA approach while the FastICA approach leads to the best classification performance with the improvement of nearly 5% compared with the combination approach. 2009 Copyright SPIE - The International Society for Optical Engineering.
	金融票据识别系统的应用研究学位论文 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, 2006 作者: 殷绪成收藏 \| 浏览/下载：63/0 \| 提交时间：2015/09/02 金融票据识别系统票据分类字符切分与识别特征集成文档图像压缩 Financial Document Analysis and Recognition System Form Classification Character Segmentation and Recognition Feature Combination Document Image Coding