[4] HAN J H, LIU S B, QIN G, et al.A local contrast method combined with adaptive background estimation for infrared small target detection［J］.IEEE Geoscience and Remote Sensing Letters, 2019, 16(9):1442-1446.
[6] FAN Z L, BI D Y, XIONG L, et al.Dim infrared image enhancement based on convolutional neural network［J］.Neurocomputing, 2018, 272:396-404.
[7] LIU Q, LU X H, HE Z Y, et al.Deep convolutional neural networks for thermal infrared object tracking［J］.Know-ledge-Based Systems, 2017, 134:189-198.
[8] ZHANG P, WANG X W, WANG X Y, et al.Infrared small target detection based on spatial-temporal enhancement using quaternion discrete cosine transform［J］.IEEE Access, 2019, 7:54712-54723.
[10] NIE Q F, JIN L Z, FEI S M.Probability estimation for multi-class classification using AdaBoost［J］.Pattern Recognition, 2014, 47(12):3931-3940.
[14] NIE Q F, JIN L Z, FEI S M, et al.A forward stagewise neural network algorithm for multi-class object recognition［C］//Proceedings of the 11th World Congress on Intelligent Control and Automation.Shenyang:IEEE, 2014:5092-5096.
[15] NIE Q F, JIN L Z, FEI S M, et al.Neural network for multi-class classification by boosting composite stumps［J］.Neurocomputing, 2015, 149:949-956.
[16] HASTIE T, TIBSHIRANI R, FRIEDMAN J.The elements of statistical learning［M］.2nd ed.［S.l.］:Springer, 2009.
[17] FRIEDMAN J, HASTIE T, TIBSHIRANI R.Additive logistic regression:a statistical view of boosting (with discussion and a rejoinder by the authors)［J］.The Annals of Statistics, 2000, 28(2):337-407.
[18] CHANG C C, LIN C J.LIBSVM:a library for support vector machines［J］.ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):1-27.
[19] NIE Q F, JIN L Z, DONG Y C, et al.Multi-class boosting based on phase-out model［C］//Proceedings of the 32nd Chinese Control Conference.Xian:IEEE, 2013:463-467.
