Real Time Feature Extraction Method of Developmental Robot
XIE Ziqiang1, GE Weimin1, WANG Xiaofeng1, LIU Jun1, LIU Zengchang2
1. Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System, Tianjin 300384, China;
2. China Automobile Industry Engineering Co., LTD, Tianjin 300113, China
谢自强, 葛为民, 王肖锋, 刘军, 刘增昌. 发展型机器人实时特征提取方法研究[J]. 机器人, 2017, 39(2): 189-196.DOI: 10.13973/j.cnki.robot.2017.0189.
XIE Ziqiang, GE Weimin, WANG Xiaofeng, LIU Jun, LIU Zengchang. Real Time Feature Extraction Method of Developmental Robot. ROBOT, 2017, 39(2): 189-196. DOI: 10.13973/j.cnki.robot.2017.0189.
Abstract:For the incremental computation and real-time problems of the feature extraction in the self-learning process of developmental robot, an incremental BDPCA (bidirectional principal component analysis) algorithm based on CCIPCA (candid covariance-free incremental principal component analysis) and BDPCA algorithms is proposed. The iterative calculation method is also adopted with the incremental computation ability. In the proposed algorithm, the 2-dimensional original image matrix is taken as the processing object directly, which effectively reduces the computation cost and shortens the running time. To verify the proposed algorithm, the support vector machine method is used to classify the building blocks grasped by the manipulator. The experimental results show that the algorithm is effective and can increase the average classification rate to 90%. The processing speed is approximately 26 frames per second, which can meet the real-time processing needs of developmental robots.
[1] 钱夔,宋爱国,章华涛,等.基于自主发育神经网络的机器人室内场景识别[J].机器人,2013,35(6):703-708, 743.Qian K, Song A G, Zhang H T, et al. Robot indoor scenes recognition based on autonomous developmental neural network[J]. Robot, 2013, 35(6):703-708,743.
[2] 张欣,周昌乐,江敏,等.受人类婴儿发育启发的机器人手眼协调方法[J].机器人,2014,36(2):185-193.Zhang X, Zhou C L, Jiang M, et al. An approach to robot hand-eye coordination inspired by human infant development[J]. Robot, 2014, 36(2):185-193.
[3] Asada M, Hosoda K, Kuniyoshi Y, et al. Cognitive developmental robotics:A survey[J]. IEEE Transactions on Autonomous Mental Development, 2009, 1(1):12-34.
[4] Delac K, Grgic M, Grgic S. Statistics in face recognition:Analyzing probability distributions of PCA, ICA and LDA performance result[C]//4th International Symposium on Image and Signal Processing and Analysis. Zagreb, Croatia:University of Zagreb, 2005:289-294.
[5] Weng J Y, Zhang Y L, Hwang W S. Candid covariance-free incremental principal component analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8):1034-1040.
[6] Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA:A new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1):131-137.
[7] Zuo W M, Zhang D, Wang K Q. Bidirectional PCA with assembled matrix distance metric for image recognition[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2006, 36(4):863-872.
[8] Sun Y F, Chen S Y, Yin B C. Color face recognition based on quaternion matrix representation[J]. Pattern Recognition Letters, 2011, 32(4):597-605.
[9] Yang W K, Sun C Y, Zhang L, et al. Laplacian bidirectional PCA for face recognition[J]. Neurocomputing, 2010, 74(1-3):487-493.
[10] Nguyen T H B, Kim H. Novel and efficient pedestrian detection using bidirectional PCA[J]. Pattern Recognition, 2013, 46(8):2220-2227.
[11] Cui K, Gao Q X, Zhang H L, et al. Merging model-based two-dimensional principal component analysis[J]. Neurocomputing, 2015, 168:1198-1206.
[12] Choi Y, Ozawa S, Lee M. Incremental two-dimensional kernel principal component analysis[J]. Neurocomputing, 2014, 134:280-288.
[13] Ren C X, Dai D Q. Incremental learning of bidirectional principal components for face recognition[J]. Pattern Recognition, 2010, 43(1):318-330.