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Abstract: For a long time, trouble detection and maintenance of freight cars have been completed manually by inspectors. To realize the transition from manual to computer-based detection and maintenance, we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system. Using mid-level features which are also named flexible edge arrangement (FEA) features, we first build the edge-based 2D model of the dust collectors, and then match target objects by a weighted hausdorff distance method. The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively, which can truly reflect the most basic property regions of the 3D object. Experimental results indicate that the proposed algorithm has better robustness to variable lighting, different viewing angle, and complex texture, and it shows a stronger adaptive performance. The localization correct rate of the target object is over 90%, which completely meets the need of practical applications.

[1]Amit, Y., 1994. A nonlinear variational problem for image matching. SIAM J. Sci. Comput., 15(1):207-224.

[2]Amit, Y., 2002. Sparse Models: Formulation, Training, and Statistical Properties. The MIT Press, Cambridge, US, p.121-137.

[3]Amit, Y., Grenander, U., Piccioni, M., 1991. Structural image restoration through deformable templates. J. Am. Stat. Assoc., 86(414):376-387.

[4]Bajcsy, R., Kovacic, S., 1989. Multiresolution elastic matching. Comput. Vis. Graph. Image Process., 46(1):1-21.

[5]Chesnaud, C., Réfrégier, P., Boulet, V., 1999. Statistical region snake-based segmentation adapted to different physical noise models. IEEE Trans. Pattern Anal. Mach. Intell., 21(11):1145-1157.

[6]Chui, H., Rangarajan, A., 2000. A New Algorithm for Non-rigid Point Matching. IEEE Conf. on Computer Vision and Pattern Recognition, p.44-51.

[7]de Ruvo, P., Distante, A., Stella, E., Marino, F., 2009. A GPU-Based Vision System for Real Time Detection of Fastening Elements in Railway Inspection. IEEE Int. Conf. on Image Processing, p.2333-2336.

[8]Dubuisson, M.P., Jain, A.K., 1994. A Modified Hausdorff Distance for Object Matching. Proc. 12th Int. Conf. on Pattern Recognition, p.566-568.

[9]Grenander, U., 1970. A unified approach to pattern analysis. Adv. Comput., 10(1):175-216.

[10]Hart, J.M., Resendiz, E., Freid, B., Sawadisavi, S., Barkan, C., Ahuja, N., 2008. Machine Vision Using Multi-spectral Imaging for Undercarriage Inspection of Railroad Equipment. Proc. 8th World Congress on Railway Research, p.1-8.

[11]Hartley, R.I., Kahl, F., 2009. Global optimization through rotation space search. Int. J. Comput. Vis., 82(1):64-79.

[12]Jesorsky, O., Kirchberg, K., Frischholz, R.W., 2001. Robust face detection using the Hausdorff distance. LNCS, 2091: 90-95.

[13]Kass, M., Witkin, A., Terzopoulos, D., 1988. Snakes: active contour models. Int. J. Comput. Vis., 1(4):321-331.

[14]Li, H., Hartley, R., 2007. The 3D-3D Registration Problem Revisited. IEEE 11th Int. Conf. on Computer Vision, p.1-8.

[15]Lin, K.H., Lam, K.M., Siu, W.C., 2003. Spatially eigen weighted Hausdorff distances for human face recognition. Pattern Recogn., 36(8):1827-1834.

[16]Liu, R., Wang, Y., 2005. Principle and Application of TFDS. China Railway Publication, Beijing, p.1-20 (in Chinese).

[17]Marino, F., Distante, A., Mazzeo, P.L., Stella, E., 2007. A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection. IEEE Trans. Syst. Man. Cybern. Part C Appl. Rev., 37(3):418-428.

[18]Metaxas, D., Koh, E., Badler, N.I., 1997. Multi-level shape representation using global deformations and locally adaptive finite elements. Int. J. Comput. Vis., 25(1):49-61.

[19]Olsson, C., Kahl, F., Oskarsson, M., 2009. Branch-and-bound methods for Euclidean registration problems. IEEE Trans. Pattern Anal. Mach. Intell., 31(5):783-794.

[20]Riesenhuber, M., Poggio, T., 2000. Models of object recognition. Nat. Neurosci., 3:1199-1204.

[21]Rucklidge, W.J., 1997. Efficiently locating objects using the Hausdorff distance. Int. J. Comput. Vis., 24(3):251-270.

[22]Shi, F., Yang, J., Zhu, Y., 2009. Automatic segmentation of bladder in CT images. J. Zhejiang Univ.-Sci. A, 10(2):239-246.

[23]Suk, H., Lee, S., 2013. A novel Bayesian framework for discriminative feature extraction in brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell., 35(2):286-299.

[24]Szeliski, R., Lavallée, S., 1996. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines. Int. J. Comput. Vis., 18(2):171-186.

[25]Tan, H., Zhang, Y.J., 2006. A novel weighted Hausdorff distance for face localization. Image Vis. Comput., 24(7):656-662.

[26]Yella, S., Dougherty, M., Gupta, N.K., 2009. Condition monitoring of wooden railway sleepers. Transp. Res. Part C Emerg. Technol., 17(1):38-55.

[27]Zhang, H., Yang, J., Tao, W., Zhao, H., 2011. Vision method of inspecting missing fastening components in high-speed railway. Appl. Opt., 50(20):3658-3665.

[28]Zhu, S.C., Yuille, A., 1996. Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 18(9):884-900.