Abstract
Body pixel classification is a multiclass pixel by pixel image segmentation problem that aims to classify each image pixel to its correspondent human body part. In this article we initially adopted for this problem a Multilayer Perceptron neural network (MLP) classifier using back propagation algorithm to learn network weights and biases. Then confidence intervals based on diffMax criterion are computed in order to make classification more certain. This criterion is computed by the difference between the first and second maximum value of MLP output vector.
A 92 % correct classification rate was achieved after applying confidence classification. The classification result will be integrated as an input to a human posture recognition system.
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References
Rosales, R., SclaroffInferring, S.: Body Pose without Tracking Body Parts. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (2000)
Lacassagne, L., Manzanera, A.: Motion Detection: Fast and robust algorithms for embedded systems. In: Proceedings of the 16th IEEE International Conference on Image Processing (2009)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-Time Human Pose Recognition in Parts from Single Depth Images. In: Proceedings of the IEEE Computer Vision and Pattern Recognition Conference, pp. 1297–1304 (2011)
Piccardi, M.: Background Subtraction Techniques:a review. In: Proceedings of the International Conference on Systems, Man and Cybernetics, pp. 3199–3104 (2004)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 509–522 (2002)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Mortensen, E.N., Deng, H., Shapiro, L.: A SIFT descriptor with global context. In: Computer Vision and Pattern Recognition (2005)
Amsaleg, L., Gros, P., Mezhoud, R.: Mise en base d’images indexées par des descripteurs locaux: problèmes et perspectives. In: Research report No. 1316. Institut de Recherche en Informatique et Systèmes Aléatoires, Rennes, France (2000)
Hamburg, M., Young, P.: Statistical analysis for decision making, 6th edn., Edition Technip. (1993) ISBN: 0-03-096914-X
Porle, R.R., Chekima, A., Wong, F., Sainarayanan, G.: Wavelet-based skin segmentation for detecting occluded arms in human body pose modelling system. In: Proceedings of the International Conference on Intelligent and Advanced Systems, pp. 764–769 (2007)
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Chaabani, H., Filali, W., Simon, T., Lerasle, F. (2012). Body Pixel Classification by Neural Network. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_48
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DOI: https://doi.org/10.1007/978-3-642-33503-7_48
Publisher Name: Springer, Berlin, Heidelberg
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