Machine learning algorithms application to road defects classification

[1]  Wensheng Tang,et al.  Pavement Crack Segmentation Algorithm Based on Local Optimal Threshold of Cracks Density Distribution , 2011, ICIC.

[2]  Fereidoon Moghadas Nejad,et al.  An optimum feature extraction method based on Wavelet-Radon Transform and Dynamic Neural Network for pavement distress classification , 2011, Expert Syst. Appl..

[3]  Mohamed Medhat Gaber,et al.  An entropy-based approach to enhancing Random Forests , 2013, Intell. Decis. Technol..

[4]  Ignacio Parra,et al.  Adaptive Road Crack Detection System by Pavement Classification , 2011, Sensors.

[5]  Nathalie Japkowicz,et al.  Boosting Support Vector Machines for Imbalanced Data Sets , 2008, ISMIS.

[6]  Denis N. Sidorov,et al.  A combined work optimization technology under resource constraints with an application to road repair , 2016, Autom. Remote. Control..

[7]  Aleksei V. Zhukov,et al.  On Road Defects Detection and Classification , 2016, AIST.

[8]  Anil K. Jain,et al.  A Markov random field model for classification of multisource satellite imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[9]  Heng-Da Cheng,et al.  Novel Approach to Pavement Cracking Detection Based on Neural Network , 2001 .

[10]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  B. K. Tripathy,et al.  Soft granular computing based classification using hybrid fuzzy-KNN-SVM , 2016, Intell. Decis. Technol..

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  John A. Saghri,et al.  Analysis of the Precision of Generalized Chain Codes for the Representation of Planar Curves , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  D. N. Sidorov,et al.  A ROBUST APPROACH FOR ROAD PAVEMENT DEFECTS DETECTION AND CLASSIFICATION , 2016 .

[15]  Paulo Lobato Correia,et al.  Automatic road crack segmentation using entropy and image dynamic thresholding , 2009, 2009 17th European Signal Processing Conference.

[16]  Ling Xu,et al.  Simple Procedure for Identifying Pavement Distresses from Video Images , 1994 .

[17]  Zhao Yan.  Automatic Recognition Algorithm of Pavement Defect Image Based on OTSU and Maximizing Mutual Information , 2009 .

[18]  Saverio Salerno,et al.  Automatic defects classification with p-median clustering technique , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[19]  Christian Koch,et al.  Pothole detection in asphalt pavement images , 2011, Adv. Eng. Informatics.

[20]  Manas Ranjan Patra,et al.  Network intrusion detection system: A machine learning approach , 2011, Intell. Decis. Technol..

[21]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[22]  M. Avila,et al.  Detection of defects in road surface by a vision system , 2008, MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference.

[23]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Heng-Da Cheng,et al.  Automatic Pavement Distress Setection System , 1998, Inf. Sci..

[25]  S. Chambon,et al.  Automatic Road Pavement Assessment with Image Processing: Review and Comparison , 2011 .

[26]  Sebastiano B. Serpico,et al.  A Markov random field approach to spatio-temporal contextual image classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[27]  Guanqun Bao,et al.  Road Distress Analysis using 2D and 3D Information , 2010 .

[28]  He Li.  Image Enhancement Algorithm on Ridgelet Domain in Detection of Road Cracks , 2009 .

[29]  Ivana Podnar Žarko,et al.  Tuning machine learning algorithms for content-based movie recommendation , 2015, Intell. Decis. Technol..