...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach
【24h】

Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach

机译：使用二维主成分分析的视觉学习和识别3D对象：强大而有效的方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Inspired by the conviction that the successful model employed for face recognition [M. Turk, A. Pentland, Eigenfaces for recognition, J. Cogn. Neurosci. 3(1) (1991) 71-86] should be extendable for object recognition [H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, International J. Comput. Vis. 14(1) (1995) 5-24], in this paper, it new technique called two-dimensional principal component analysis (2D-PCA) [J. Yang et al., Two-dimensional PCA: a new approach to appearance based face representation and recognition, IEEE Trans. Patt. Anal. Mach. Intell. 26(1) (2004) 131-137] is explored for 3D object representation and recognition. 2D-PCA is based on 2D image matrices rather than I D vectors so that the image matrix need not be transformed into a vector prior to feature extraction. Image covariance matrix is directly computed using the original image matrices, and its eigenvectors are derived for feature extraction. The experimental results indicate that the 2D-PCA is computationally more efficient than conventional PCA (1D-PCA) [H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, International J. Comput. Vis. 14(1) (1995) 5-24]. It is also revealed through experimentation that the proposed method is more robust to noise and occlusion. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译：受到面对面识别的成功模型的信念的启发[M.土耳其人，A. PENTLAND，识别特征缺陷，J. Cogn。 Neurosci。 3（1）（1991）71-86]应该可扩展到物体识别[H.羽毛，S.K.纳雷尔，视觉学习和识别外观，国际J.Copm。 vis。 14（1）（1995）5-24]本文，其新技术称为二维主成分分析（2D-PCA）[J。杨等人，二维PCA：IEEE Trans的外观面貌识别新方法。帕特。肛门。马赫。智能。探索36（1）（2004）131-137，用于3D对象表示和识别。图2D-PCA基于2D图像矩阵而不是I D向量，使得图像矩阵不需要在特征提取之前转换为载体。使用原始图像矩阵直接计算图像协方差矩阵，并且导出其特征提取的特征向量。实验结果表明，2D-PCA比常规PCA（1D-PCA）计算更有效[H.羽毛，S.K.纳雷尔，视觉学习和识别外观，国际J.Copm。 vis。 14（1）（1995）5-24]。还通过实验揭示了所提出的方法对噪音和闭塞更加坚固。 （c）2005年模式识别协会。 elsevier有限公司出版。保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号