Image analysis – Applications – Target tracking or detecting
Reexamination Certificate
2008-01-31
2011-11-08
Ahmed, Samir (Department: 2624)
Image analysis
Applications
Target tracking or detecting
C382S156000
Reexamination Certificate
active
08055018
ABSTRACT:
The present invention discloses an object image detection method, which uses a coarse-to-fine strategy to detect objects. The method of the present invention comprises steps: acquiring an image and pre-processing the image to achieve dimensional reduction and information fusion; using a trained filter to screen features; and sequentially using a coarse-level MLP verifier and a fine-level MLP verifier to perform a neural network image detection to determine whether the features of the image match the features of the image of a target object. The present invention simultaneously uses three mainstream image detection methods, including the statistic method, neural network method and adaboost method, to perform image detection. Therefore, the present invention has the advantages of the rapidity of the adaboost method and the accuracy of the neural network method at the same time.
REFERENCES:
patent: 6707933 (2004-03-01), Mariani et al.
patent: 6915022 (2005-07-01), Huang et al.
patent: 7139411 (2006-11-01), Fujimura et al.
patent: 7266225 (2007-09-01), Mariani et al.
patent: 7436988 (2008-10-01), Zhang et al.
patent: 2010/0165136 (2010-07-01), Johnson et al.
patent: 2010/0284619 (2010-11-01), Song et al.
patent: 420939 (2001-02-01), None
patent: 505892 (2002-10-01), None
patent: 550517 (2003-09-01), None
patent: 569148 (2004-01-01), None
patent: I226020 (2005-01-01), None
patent: I233571 (2005-06-01), None
patent: I254891 (2006-05-01), None
Henry A. Rowley, et al., Neural network-based face detection, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, pp. 23-38, 1998.
Christophe Garcia and Manolis Delakis, Convolutional face finder: A neural architecture for fast and robust face detector, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 26, pp. 140.
A. N.Rajagopalan, et al., Background Learning for Robust Face Recognition With PCA in the Presence of Cluster, IEEE Transaction on Image Processing, vol. 14, No. 6, pp. 832-843, 2005.
Paul Viola and Michael J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol. 57, No. 2, pp. 137-154, 2004.
Chengjun Liu, A Bayesian discriminating features method for face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 6, pp. 725-740, Jun. 2003.
Raphael Feraud, et al., “A fast and accurate face detector based on neural networks” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 42-52, 2001.
Masakazu Matsugu, Katsuhiko Mori, Yusuke Mitari, and Yuji Kaneda, Subject independent facial expression recognition with robust face detection using a convolutional neural network, Neural Networks, vol. 16, pp. 555-559, 2003.
Kah-Kay Sung and Tomaso Poggio, Example-based learning for view-based human face detection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, No. 1, pp. 39-51, 1998.
Rein-Lien Hsu, Mohamed Abdel-Mottaleb, and Anil K. Jain, Face detection in color images, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, No. 5, pp. 696-706, 2002.
Peichung Shih, and Chengjun Liu, Face detection using discriminating feature analysis and support vector machine, Pattern Recognition, vol. 39, pp. 260-276, 2006.
Christopher A. Waring, and Xiuwen Liu, Face Detection Using Spectral Histograms and SVMs, IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 35, No. 3, pp. 467-476, 2005.
Rong Xiao, Ming-Jing Li, and Hong-Jiang Zhang, Robust multipose face detection in images, IEEE Trans. Circuits and Systems for Video Technology, vol. 14, No. 1, pp. 31-41, 2004.
Yongmin Li, Shaogang Gong, Jamie Sherrah, and Heather Liddell, Support vector machine based multi-view face detection and recognition, Image and Vision Computing, vol. 22, pp. 413-427, 2004.
Bernd Heisele, Thomas Serre, Sam Prentice, and Tomaso Poggio, Hierarchical classification and feature reduction for fast face detection with support vector machines, Pattern Recognition, vol. 36, pp. 2007-2017, 2003.
Chen Ying-Nong
Han Chin-Chuan
Ahmed Samir
National Chiao Tung University
Rashidian Mehdi
Rosenberg , Klein & Lee
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