QQ客服 官方微博 反馈留言

浏览/检索结果: 共3条，第1-3条 帮助 限定条件 专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China Wang D.; Ge W.; Wang Y. 收藏  |  浏览/下载：16/0  |  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 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 Ge W.; Wang D.; Cheng Y.; Zhu M. 收藏  |  浏览/下载：26/0  |  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.   A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Han Q.-L.; Zhu M.; Yao Z.-J. 收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25 Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However  3. applying the standard Hough transform equation to every point of the input image edge  4. according to the local threshold  6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes  traditional Hough transform can only detect the lines  2. quantizing the parameter space  and extracting a group of maximums according to the global threshold  eliminating spurious peaks which are caused by the spreading effects  and will improve the precision of tracking.  cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations  Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information  as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately  5. fixing on the endpoints of the segments according to the dynamic clustering rule  which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image  parameter and line-segment spaces