Cheng H D, Miyojim M. Automatic pavement distress detection system[J]. Journal of Information Science, 1998, 108(1-4):219-240.
[2]
Yan M D, Bo S B, He Y Y. A method of image detection and analysis for pavement crack based on morphology[J]. Journal of Engineering Graphics, 2008, 29(2):142-147.[闫茂德,伯绍波,贺昱曜.一种形态学的路面裂缝图像检测与分析方法[J].工程图学学报,2008,29(2):142-147.]
[3]
Li Q Q, Liu X L. Novel approach to pavement image segmentation based on neighboring difference histogram method[C]//Proceeding of International Conference on Image and Signal Processing. Sanya, China:IEEE, 2008:792-796.
[4]
Ma C X, Zhao C X, Di F, et al. Road crack detection under natural environment[J]. Journal of Engineering Graphics, 2011, 32(4):20-26.[马常霞,赵春霞,狄峰,等.自然环境下路面裂缝识别[J].工程图学学报,2011,32(4):20-26.]
[5]
Xu W, Tang Z M, Xu D, et al. Integrating multi-features fusion and gestalt principles for pavement crack detection[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(1):147-156.[徐威,唐振民,徐丹,等.融合多特征与格式塔理论的路面裂缝检测[J].计算机辅助设计与图形学学报,2015,27(1):147-156.]
[6]
Fereidoon M N, Hamzeh Z. An optimum feature extraction method based on wavelet-radon transform and dynamic neural network for pavement distress classification[J]. Expert Systems with Applications, 2011, 38(8):9442-9460.
[7]
Ma C X, Zhao C X, Hu Y, et al. Road crack detection based on NSCT and morphology[J]. Journal of Computer-Aided Design & Computer Graphics, 2009, 21(12):1761-1767.[马常霞,赵春霞,胡勇,等.结合NSCT和图像形态学的路面裂缝检测[J].计算机辅助设计与图形学学报,2009,21(12):1761-1767.]
[8]
Fujita Y, Hamamoto Y. A robust automatic crack detection method from noisy concrete surfaces[J]. Machine Vision and Applications, 2011, 22(2):245-254.
[9]
Xu W, Tang Z M, Zhou J, et al. Pavement crack detection based on saliency and statistical features[C]//Proceeding of International Conference on Image Processing. Melbourne:IEEE, 2013:4093-4097.
[10]
Xu W, Tang Z M, Lv J Y. Pavement crack detection based on image saliency[J]. Journal of Image and Graphics, 2013, 18(1):69-77.[徐威,唐振民,吕建勇.基于图像显著性的路面裂缝检测[J].中国图像图形学报,2013,18(1):69-77.] [DOI:10.11834/jig.20130109.]
[11]
Li Q Q, Zou Q, Zhang D Q, et al. FoSA:F* seed-growing approach for crack-line detection from pavement images[J]. Image and Vision Computing, 2011, 29(12):861-872.
[12]
Zou Q, Cao Y, Li Q Q, et al. CrackTree:automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2011, 33(3):227-238.
[13]
Chu X M, Wang R B. Asphalt pavement surface distress image recognition based on neural network[J]. Journal of Wuhan University of Technology, 2004, 28(3):69-77.[初秀明,王荣本.基于神经网络的沥青路面破损图像识别研究[J].武汉理工大学学报,2004,28(3):69-77.]
[14]
Gavilan M, Balcones D, Marcos O, et al. Adaptive road crack detection system by pavement classification[J]. Sensors, 2011, 11(10):9628-9657.
[15]
Oliveira H, Correia P L. Automatic road crack detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1):155-168.
[16]
Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[17]
Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of International Conference on Machine Learning. New York, USA:ACM Press, 2008:1096-1103.
[18]
Fisher R A. The use of multiple measurements in taxonomic problem[J]. Journal of Annals Eugenics,1936,7(2):179-188.
[19]
Hastie T, Tibshirani R. Discriminant analysis by Gaussian mixtures[J]. Journal of the Royal Statistical Society,1996,58(1):155-176.
[20]
Zhu M, Martinez A. Subclass discriminant analysis[J]. Journal of IEEE Transactions on Pattern Analysis and Machine Learning,2006,28(8):1274-1286.
[21]
更多...
[22]
Tao Y T, Yang J, Chang H Y. Enhanced iterative projection for subclass discriminant analysis under EM-alike framework[J]. Pattern Recognition,2014,47(3):1113-1125.
[23]
Oliveira H, Correia P L. CrackIT-an image processing toolbox for crack detection and characterization[C]//Proceeding of International Conference on Image Processing. Paris, France:IEEE, 2014:798-802.
[24]
Quoc V L, Jiquan N, Adam C, et al. On optimization methods for deep learning[C]//Proceeding of International Conference on Machine Learning. Bellevue, USA:ACM, 2011:265-272.
[25]
Boyd S, Vandenberghe L. Convex Optimization[M]. New York:Cambridge University Press, 2004:678-680.
