期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

计算旋转Harr型特征的积分图像算法改进 被引量：8

An Improved Algorithm of Integral Image for Computing Rotated Harr-Like Features
在线阅读 下载PDF
导出
摘要 通过改进计算旋转Harr型特征的积分图像算法,降低了其计算复杂度,实现了遍历原图像一次即可获得图像直立矩形窗口和45旋转矩形窗口的积分图像,实验证明算法的改进是有效的。 An improved algorithm of integral image for computing rotated Harr- like features is proposed. Not only the calculating complcxity is reduced, but also the integral images of upright windows and 45° rotated windows can be gained by scarching through the origin image once. Experimental results demonstrate the efficiency of this improved algorithm.
作者 邵平 杨路明 曾耀荣
机构地区 中南大学信息科学与工程学院 玉林师范学院物理与信息科学系
出处 《计算机技术与发展》 2006年第11期146-147,181,共3页 Computer Technology and Development
基金 广西教育厅面上项目(桂教科研[2004]20号) 玉林师范学院重点科研资助项目(05DWX16) 玉林师范学院科研资助项目(05YBWX23)
关键词 Harr型特征 积分图像 算法改进 人脸检测 Hart- like features integral image improved algorithm face detection
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献5

  • 1Crow F.Summed-area tables for texture mapping[J].SIGGRAPH,1984,18 (3):207-212.
  • 2Viola P,Jones M.Rapid object detection using a boosted cascade of simple features[C]//In Proc.of the IEEE Computer Vision and Pattern Recognition.Cambridge,Britain:[s.n.],2001:511-518.
  • 3Lienhart R,Kuranov A,Pisarevsky V.Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection[C]//In Proc.25th German Pattern Recognition Symposium.Magdeburg,Germany:[s.n.],2003:297-304.
  • 4Lienhart R,Maydt J.An Extended Set of Haar-like Features for Rapid Object Detection[C]//In Proc.of the IEEE Conf.on Image Processing.New York,USA:[s.n.],2002:155-162.
  • 5武勃,黄畅,艾海舟,劳世竑.基于连续Adaboost算法的多视角人脸检测[J].计算机研究与发展,2005,42(9):1612-1621. 被引量：66

二级参考文献13

  • 1B. Moghaddam, A. Pentlan. Beyond linear eigenspaces: Bayesian matching for face recognition. In: Face Recognition: From Theory to Application. New York: Springer-Verlag 1998. 230～243.
  • 2H. A. Rowley. Neural network-based human face detection:[Ph. D. dissertation]. Pittsburgh, USA: Carnegie Mellon University, 1999.
  • 3R. Feraud, O.J. Bernier, Jean-Emmanuel Viallet, et al. A Fast and accurate face detector based on neural networks. IEEE Trans.Pattern Analysis and Machine Intelligence, 2001, 23(1): 42～53.
  • 4H. Schneiderman, T. Kanade. A statistical method for 3D object detection applied to faces and cars. IEEE Conf. Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina,2000.
  • 5E. Osuna, R. Freund, F. Girosi. Training support vector machines: An application to face detection. IEEE Conf. Computer Vision and Pattern Recognition, Puerto Rico, 1997.
  • 6V.P. Kumar, T. Poggio. Learning-based approach to real time tracking and analysis of faces. http: ∥ cbcl. mit. edu/cbcl/publications/ai- publications, 1999.
  • 7P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features. IEEE Conf. Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, 2001.
  • 8Y. Freund, R. E. Schapire. Experiments with a new boosting algorithm. In: Proc. the 13th Conf. Machine Learning. San Francisco: Morgan Kaufmann, 1996. 148～156.
  • 9R.E. Schapire, Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 1999, 37 (3) .297～336.
  • 10Y. Li, S. Gong, H. Liddell. Support vector regression and classification based multi-view face detection and recognition.IEEE Conf. Automatic Face and Gesture Recognition, Grenoble,France, 2000.

共引文献65

同被引文献65

引证文献8

二级引证文献42

计算机技术与发展
2006年 第11期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部