Neural Networks for Handwriting recognition Table 5 Summarized results from the offline French handwriting recognition competition Word accu HMM+MLP Combination 83.17% Non-Symmetric HMM 83. 17 % CTC (MDLSTM) 93.17% A summary of the results appear in Tables 3-5. As can be seen, the approach de- scribed in this chapter always outperformed the other systems in the offline case This observation is very promising, because the system just uses the 2- dimensional raw pixel data as an input. For the online competition (Table 3) a commercial recognizer performed better han the ctC approach. However, if the CtC system would be combined with State-of-the-Art preprocessing and feature extraction methods, it would probably reach a higher performance. This observation has been made in [39], where ex tended experiments to those in Section 1. 4. I have been performed Having a look at the calculation time(milliseconds per text line)also reveals very promising results. The MDLSTM combined with CTC was ar recognizers in the competitions. Using some pruning strategies could further in crease the recognition speed 5 Conclusion This chapter described a novel approach for recognizing unconstrained handwrit ten text, using a recurrent neural network. The key features of the network are bidirectional Long Short-Term Memory architecture, which provides access to long range, bidirectional contextual information, and the Connectionist Temporal Classification output layer, which allows the network to be trained on unseg mented sequence data. In experiments on online and offline handwriting data, the new approach outperformed state-of-the-art HMM-based classifiers and several other recognizers. We conclude that this system represents a significant advance in he field of unconstrained handwriting recognition, and merits further research. toolkit implementing the presented architecture is freely available to the public [1 Seiler, R, Schenkel, M, Eggimann, F: Off-line handwriting recognition ompared with on-line recognition. In: ICPR 1996 dings of the International Conference on Pattern Recognition (ICPR 1996). -7472, P 505. IEEE Com- puter Society, Washington, DC, USA(1996) 2] Tappert, C, Suen, C, Wakahara, T: The state of the art in online handwriting recog- nition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(8) 787-808(190)
Neural Networks for Handwriting Recognition 21 Table 5 Summarized results from the offline French handwriting recognition competition System Word Accuracy HMM+MLP Combination 83.17% Non-Symmetric HMM 83.17 % CTC (MDLSTM) 93.17% A summary of the results appear in Tables 3-5. As can be seen, the approach described in this chapter always outperformed the other systems in the offline case. This observation is very promising, because the system just uses the 2- dimensional raw pixel data as an input. For the online competition (Table 3) a commercial recognizer performed better than the CTC approach. However, if the CTC system would be combined with State-of-the-Art preprocessing and feature extraction methods, it would probably reach a higher performance. This observation has been made in [39], where extended experiments to those in Section 1.4.1 have been performed. Having a look at the calculation time (milliseconds per text line) also reveals very promising results. The MDLSTM combined with CTC was among the fastest recognizers in the competitions. Using some pruning strategies could further increase the recognition speed. 5 Conclusion This chapter described a novel approach for recognizing unconstrained handwritten text, using a recurrent neural network. The key features of the network are the bidirectional Long Short-Term Memory architecture, which provides access to long range, bidirectional contextual information, and the Connectionist Temporal Classification output layer, which allows the network to be trained on unsegmented sequence data. In experiments on online and offline handwriting data, the new approach outperformed state-of-the-art HMM-based classifiers and several other recognizers. We conclude that this system represents a significant advance in the field of unconstrained handwriting recognition, and merits further research. A toolkit implementing the presented architecture is freely available to the public. References [1] Seiler, R., Schenkel, M., Eggimann, F.: Off-line cursive handwriting recognition compared with on-line recognition. In: ICPR 1996: Proceedings of the International Conference on Pattern Recognition (ICPR 1996), vol. IV-7472, p. 505. IEEE Computer Society, Washington, DC, USA (1996) [2] Tappert, C., Suen, C., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(8), 787–808 (1990)
M. Liwicki. A Graves and H. Bunke [3 Plamondon, R, Srihari, S N: On-line and off-line handwriting recognition: A com- prehensive survey. IEEE Trans. Pattern Anal. Mach Intell. 22(1), 63-84(2000) [4] Vinciarelli, A A survey on off-line cursive script recognition. Pattern Recog tion35(7),1433-144602002) [5] Bunke, H: Recognition of cursive roman handwriting- past present and future. In c.7th Int. Conf. on Document Analysis and Recognition, vol. l, pp 448-459 2003 [6] Guyon, I, Schomaker, L, Plamondon, R, Liberman, M, Janet, S: Unipen project of on-line data exchange and recognizer benchmarks. In: Proc. 12th Int. Conf on Pattern Recognition, pp 29-33(1994) [71 Hu, J, Lim, S, Brown, M: Writer independent on-line handwriting recognition using an HMM approach. Pattern Recognition 33(1), 133-147(2000) [8] Bahlmann, C, Burkhardt, H. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal and Mach Intell. 26(3), 299-310(2004) [91 Bahlmann, C, Haasdonk, B, Burkhardt, H. Online handwriting recognition with upport vector machines-a kernel approach. In: Proc. 8th Int. Workshop on Frontiers in Handwriting Recognition, pp 49-54(2002) [10] Wilfong, G, Sinden, F, Ruedisueli, L On-line recognition of handwritten symbols. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 935-940 (1996) [11 Sayre, K M: Machine recognition of handwritten words: A project report. Pattern Recognition5(3),213-228(1973) [12] Schomaker, L: Using stroke-or character-based self-organizing maps in the recogni- tion of on-line, connected cursive script. Pattern Recognition 26(3), 443-450(1993) [13] Kavallieratou, E, Fakotakis, N, Kokkinakis, G. An unconstrained handwriting rec- gnition system. Int Journal on Document Analysis and Recognition 4(4), 226-24 [14] Bercu, S, Lorette, G: On-line handwritten word recognition: An approach based on hidden Markov models. In: Proc. 3rd Int. Workshop on Frontiers in Handwriting Recognition, Pp 385-390(1993) [15] Starner, T, Makhoul, J, Schwartz, R, Chou, G. Online cursive handwriting recogni tion using speech recognition techniques. In: Int. Conf. on Acoustics, Speech and Signal Processing, vol. 5, pp. 125-128(1994) [16] Hu, J, Brown, M, Turin, W: HMM based online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 1039-1045(1996) [17] Marti, U.-V, Bunke, H: Using a statistical language model to improve the perfor- rn Recognition and Artificial Intelligence 15, 65-90 (2001) [18] Schenkel, M, Guyon, I, Henderson, D. On-line cursive script recognition using time delay neural networks and hidden Markov models. Machine Vision and Applica tions8,215-223(1995) [ 19] El-Yacoubi, A, Gilloux, M, Sabourin, R, Suen, C: An HMM-based approach for off-line unconstrained handwritten word modeling and recognition. IEEE Transac- tions on Pattern Analysis and Machine Intelligence 21(8), 752-760(1999) [20] Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in peech recognition. Proc. of the IEEE 77(2), 257-286(1989)
22 M. Liwicki, A. Graves, and H. Bunke [3] Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000) [4] Vinciarelli, A.: A survey on off-line cursive script recognition. Pattern Recognition 35(7), 1433–1446 (2002) [5] Bunke, H.: Recognition of cursive roman handwriting - past present and future. In: Proc. 7th Int. Conf. on Document Analysis and Recognition, vol. 1, pp. 448–459 (2003) [6] Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: Unipen project of on-line data exchange and recognizer benchmarks. In: Proc. 12th Int. Conf. on Pattern Recognition, pp. 29–33 (1994) [7] Hu, J., Lim, S., Brown, M.: Writer independent on-line handwriting recognition using an HMM approach. Pattern Recognition 33(1), 133–147 (2000) [8] Bahlmann, C., Burkhardt, H.: The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal. and Mach. Intell. 26(3), 299–310 (2004) [9] Bahlmann, C., Haasdonk, B., Burkhardt, H.: Online handwriting recognition with support vector machines - a kernel approach. In: Proc. 8th Int. Workshop on Frontiers in Handwriting Recognition, pp. 49–54 (2002) [10] Wilfong, G., Sinden, F., Ruedisueli, L.: On-line recognition of handwritten symbols. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 935–940 (1996) [11] Sayre, K.M.: Machine recognition of handwritten words: A project report. Pattern Recognition 5(3), 213–228 (1973) [12] Schomaker, L.: Using stroke- or character-based self-organizing maps in the recognition of on-line, connected cursive script. Pattern Recognition 26(3), 443–450 (1993) [13] Kavallieratou, E., Fakotakis, N., Kokkinakis, G.: An unconstrained handwriting recognition system. Int. Journal on Document Analysis and Recognition 4(4), 226–242 (2002) [14] Bercu, S., Lorette, G.: On-line handwritten word recognition: An approach based on hidden Markov models. In: Proc. 3rd Int. Workshop on Frontiers in Handwriting Recognition, pp. 385–390 (1993) [15] Starner, T., Makhoul, J., Schwartz, R., Chou, G.: Online cursive handwriting recognition using speech recognition techniques. In: Int. Conf. on Acoustics, Speech and Signal Processing, vol. 5, pp. 125–128 (1994) [16] Hu, J., Brown, M., Turin, W.: HMM based online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 1039–1045 (1996) [17] Marti, U.-V., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. Journal of Pattern Recognition and Artificial Intelligence 15, 65–90 (2001) [18] Schenkel, M., Guyon, I., Henderson, D.: On-line cursive script recognition using time delay neural networks and hidden Markov models. Machine Vision and Applications 8, 215–223 (1995) [19] El-Yacoubi, A., Gilloux, M., Sabourin, R., Suen, C.: An HMM-based approach for off-line unconstrained handwritten word modeling and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(8), 752–760 (1999) [20] Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)
Neural Networks for Handwriting recognition [211 Bourbakis, N.G.: Handwriting recognition using a reduced character method and neural nets. In: Proc. SPIE Nonlinear Image Processing VI, vol 2424, pp. 592-601 [22] Bourlard, H, Morgan, N: Connectionist Speech Recognition: A Hybrid Approach Kluwer Academic Publishers(1994) [23] Bengio, Y: Markovian models for sequential data. Neural Computing Surveys 2, 129-162(1999 [24] Brakensiek, A, Kosmala, A, willett, D, Wang, W, Rigoll, G. Performance evalua tion of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data. In: Proc 5th Int Conf. on Document Analysis and Recog tion, Bangalore, India, pp. 446-449(1999) 25] Marukatat, s, Artires, T, Dorizzi, B, Gallinari, P. Sentence recognition through hy brid neuro-markovian modelling In: Proc. 6th Int Conf on Document Analysis and Recognition, pp 731-735(2001) [26] Jaeger, S, Manke, S, Reichert, J, Waibel, A Online handwriting recognition: the NPen++ recognizer. Int. Journal on Document Analysis and Recognition 3(3), 169-180(2001) [27 Caillault, E, Viard-Gaudin, C, Ahmad, A.R.: MS-TDNN with global discriminant trainings. In: Proc &th Int Conf on Document Analysis and Recognition, pp. 856- 861(2005) [28] Senior, A w, Fallside, F: An off-line cursive script recognition system using recur- rent error propagation networks. In: International Workshop on Frontiers in Handwriting Recognition, Buffalo, NY, USA, pp 132-141(1993 [29] Senior, A.W., Robinson, A.J.: An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 309-321 (1998) [30] Schenk, J, Rigoll, G. Novel hybrid NN/HMM modelling techniques for on-line handwriting recognition. In: Proc. 1Oth Int. Workshop on Frontiers in Handwriting Recognition, pp 619-623(2006) [31 IAM-OnDB an on-line English sentence database acquired from handwritten text on a whiteboard, In: Proc. 8th Int Conf on Document Analysis and Recognition, pp [32] The IAM-database: an English sentence database for offline handwriting recognition. Int Journal on Document Analysis and Recognition 5, 39-46(2002) [33 Liwicki, M, Bunke, H: Handwriting recognition of whiteboard notes- studying the influence of training set size and type. Int. Journal of Pattern Recognition and Arti cial Intelligence 21(1), 83-98(2007) [34] Graves, A: Supervised Sequence Labelling with Recurrent Neural Networks, Ph D thesis, Technical University of Munich(2008) [35] Schuster, M, Paliwal, K.K. Bidirectional recurrent neural networks. IEEE Trans. Signal Processing 45, 2673-2681(1997) [36] Graves, A, Fernandez, s, Gomez, F, Schmidhuber, J: Connectionist temporal clas sification: labeling unsegmented sequence data with recurrent neural networks. I Proc. Int Conf on Machine Learning, pp 369-376(2006) [371 Pitman, J.A. Handwriting recognition: Tablet pc text input. Computer 40(9), 49-54 2007) [38] Proc. 10th Int Conf on Document Analysis and Recognition(2009)
Neural Networks for Handwriting Recognition 23 [21] Bourbakis, N.G.: Handwriting recognition using a reduced character method and neural nets. In: Proc. SPIE Nonlinear Image Processing VI, vol. 2424, pp. 592–601 (1995) [22] Bourlard, H., Morgan, N.: Connnectionist Speech Recognition: A Hybrid Approach. Kluwer Academic Publishers (1994) [23] Bengio, Y.: Markovian models for sequential data. Neural Computing Surveys 2, 129–162 (1999) [24] Brakensiek, A., Kosmala, A., Willett, D., Wang, W., Rigoll, G.: Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data. In: Proc. 5th Int. Conf. on Document Analysis and Recognition, Bangalore, India, pp. 446–449 (1999) [25] Marukatat, S., Artires, T., Dorizzi, B., Gallinari, P.: Sentence recognition through hybrid neuro-markovian modelling. In: Proc. 6th Int. Conf. on Document Analysis and Recognition, pp. 731–735 (2001) [26] Jaeger, S., Manke, S., Reichert, J., Waibel, A.: Online handwriting recognition: the NPen++ recognizer. Int. Journal on Document Analysis and Recognition 3(3), 169–180 (2001) [27] Caillault, E., Viard-Gaudin, C., Ahmad, A.R.: MS-TDNN with global discriminant trainings. In: Proc. 8th Int. Conf. on Document Analysis and Recognition, pp. 856– 861 (2005) [28] Senior, A.W., Fallside, F.: An off-line cursive script recognition system using recurrent error propagation networks. In: International Workshop on Frontiers in Handwriting Recognition, Buffalo, NY, USA, pp. 132–141 (1993) [29] Senior, A.W., Robinson, A.J.: An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 309–321 (1998) [30] Schenk, J., Rigoll, G.: Novel hybrid NN/HMM modelling techniques for on-line handwriting recognition. In: Proc. 10th Int. Workshop on Frontiers in Handwriting Recognition, pp. 619–623 (2006) [31] IAM-OnDB an on-line English sentence database acquired from handwritten text on a whiteboard, In: Proc. 8th Int. Conf. on Document Analysis and Recognition, pp. 956–961 (2005) [32] The IAM-database: an English sentence database for offline handwriting recognition. Int. Journal on Document Analysis and Recognition 5, 39–46 (2002) [33] Liwicki, M., Bunke, H.: Handwriting recognition of whiteboard notes – studying the influence of training set size and type. Int. Journal of Pattern Recognition and Artificial Intelligence 21(1), 83–98 (2007) [34] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks., Ph.D. thesis, Technical University of Munich (2008) [35] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Processing 45, 2673–2681 (1997) [36] Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labeling unsegmented sequence data with recurrent neural networks. In: Proc. Int. Conf. on Machine Learning, pp. 369–376 (2006) [37] Pitman, J.A.: Handwriting recognition: Tablet pc text input. Computer 40(9), 49–54 (2007) [38] Proc. 10th Int. Conf. on Document Analysis and Recognition (2009)
M. Liwicki. A Graves and H. Bunke [391 Graves, A, Liwicki, M, Fernandez, S, Bertolami, R, Bunke, H, Schmidhuber, J: A novel connectionist system for unconstrained handwriting recognition. IEEE Transac- tions on Pattern Analysis and Machine Intelligence 31(5),855-868(2009) 40] Hochreiter, S, Schmidhuber, J. Long Short-Term Memory. Neural Computa tion9(8),1735-1780(1997) [411 Gers, F: Long Short-Term Memory in Recurrent Neural Networks. Ph. D thesis, EPFL(2001) 42] Graves, A, Schmidhuber, J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5-6), 602-610 (2005) [431 Graves, A, Fernandez, s, Schmidhuber, J: Multidimensional recurrent neural net orks. In: Proc. Int Conf on Artificial Neural Networks(2007) [44] Baldi, P, Pollastri, G: The principled design of large-scale recursive neural network architectures-DAG-RNNs and the protein structure prediction problem. J. Mach. Learn.Res.4,575-602(2003) [45] Reisenhuber, M, Poggio, T: Hierarchical models of object recognition in cortex. Na- tureNeuroscience 2(11), 1019-1025(1999) [46] LeCun, Y, Bottou, L, Bengio, Y, Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278-2324(1998) [471 Graves, A, Schmidhuber, J. Offline handwriting recognition with multidimensional recurrent neural networks. Advances in Neural Information Processing Systems 21 545-552(2009)
24 M. Liwicki, A. Graves, and H. Bunke [39] Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5), 855–868 (2009) [40] Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Computation 9(8), 1735–1780 (1997) [41] Gers, F.: Long Short-Term Memory in Recurrent Neural Networks. Ph.D.thesis, EPFL (2001) [42] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5-6), 602–610 (2005) [43] Graves, A., Fernández, S., Schmidhuber, J.: Multidimensional recurrent neural networks. In: Proc. Int. Conf. on Artificial Neural Networks (2007) [44] Baldi, P., Pollastri, G.: The principled design of large-scale recursive neural network architectures–DAG-RNNs and the protein structure prediction problem. J. Mach. Learn. Res. 4, 575–602 (2003) [45] Reisenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. NatureNeuroscience 2(11), 1019–1025 (1999) [46] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) [47] Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. Advances in Neural Information Processing Systems 21, 545–552 (2009)
Chapter 3 Moving object Detection from mobile Platforms Using Stereo Data Registration Angel D Sappa, David Geronimo,, Fadi Dornaika4 Mohammad Rouhani, and Antonio M. Lopez,2 Computer Vision Center Universitat Autonoma de Barcelona, 08193, Bellaterra, Barcelona, Spai 2 Computer Science Depa artment Universitat Autonoma de Barcelona, 08193 Bellaterra, B IKERBASQUE, Basque Foundation for Science, Bilbao, Spain (asappa, geronimo, rouhani, antonio)@cvc. uab. es, fadi dornaikadehu es Abstract. This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally ex pensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, mov objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like ve- hicles or pedestrians in different urban scenarios. 1 Introduction The detection of moving objects in dynamic environments is generally tackled by first modelling the background. Then, foreground objects are directly obtained by performing an image subtraction(e. g,[14],[15],[321). An extensive survey on mo- tion detection algorithms can be found in [21]. In general, most of the approaches assume stationary cameras, which means all frames are registered in the same co- ordinate system. However, when the camera moves, the problem becomes intricate since it is unfeasible to have a unique background model. In such a case, moving object detection is generally tackled by compensating the camera motion so that all frames from a given video sequence, obtained from a moving camera/platform, are referred to the same reference system(e. g, [7], [27)) Moving object detection from a moving camera is a challenging problem in com- puter vision, having a number of applications in different domains: mobile robots M.R. Ogiela and L C Jain(Eds ) Computational Intelligence Paradigms, SCI 386, pp 25-37 C Springer-Verlag Berlin Heidelberg 2012
Chapter 3 Moving Object Detection from Mobile Platforms Using Stereo Data Registration Angel D. Sappa1, David Ger´onimo1,2, Fadi Dornaika3,4, Mohammad Rouhani1, and Antonio M. L´opez1,2 1 Computer Vision Center Universitat Aut`onoma de Barcelona, 08193, Bellaterra, Barcelona, Spain 2 Computer Science Department Universitat Aut`onoma de Barcelona, 08193, Bellaterra, Barcelona, Spain 3 University of the Basque Country, San Sebastian, Spain 4 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain {asappa,dgeronimo,rouhani,antonio}@cvc.uab.es, fadi dornaika@ehu.es Abstract. This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. 1 Introduction The detection of moving objects in dynamic environments is generally tackled by first modelling the background. Then, foreground objects are directly obtained by performing an image subtraction (e.g., [14], [15], [32]). An extensive survey on motion detection algorithms can be found in [21]. In general, most of the approaches assume stationary cameras, which means all frames are registered in the same coordinate system. However, when the camera moves, the problem becomes intricate since it is unfeasible to have a unique background model. In such a case, moving object detection is generally tackled by compensating the camera motion so that all frames from a given video sequence, obtained from a moving camera/platform, are referred to the same reference system (e.g., [7], [27]). Moving object detection from a moving camera is a challenging problem in computer vision, having a number of applications in different domains: mobile robots M.R. Ogiela and L.C. Jain (Eds.): Computational Intelligence Paradigms, SCI 386, pp. 25–37. springerlink.com c Springer-Verlag Berlin Heidelberg 2012