Bibliography of Jouko Lampinen February 19, 2010

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1 Bibliography of Jouko Lampinen February 19, 2010 Publications [1] Erkki Oja and Jouko Lampinen. A fast local PPF restoration filter. In Proc. Int. Conf. on Acoustics, Speech, Signal Processing, pages , [2] Erkki Oja and Jouko Lampinen. Image restoration by fast local convolution. In Image Analysis and Processing II, pages Plenum Press, New York and London, [3] Jouko Lampinen. Digital image restoration by fast local convolution filters. Master s thesis, University of Kuopio, Kuopio, Finland, [4] Jouko Lampinen. Optimal FIR approximation for frequency domain image restoration filters. In Proc. Second Annual Meeting of SIAM Nordic Section, Helsinki, Finland, August [5] Jouko Lampinen and Erkki Oja. Optimal FIR restoration filters with real or integer coefficients. Technical Report 13, Lappeenranta Univ. of Technology, Dept. of Information Technology, [6] Jouko Lampinen and Erkki Oja. Fast self-organization by the probing algorithm. In Proceedings of the First International Joint Conference on Neural Networks, Washington, DC, volume II, pages , San Diego, IEEE, IEEE TAB Neural Network Committee. [7] Jouko Lampinen and Erkki Oja. Self-organizing maps for spatial and temporal AR models. In Matti Pietikäinen and Juha Röning, editors, Proc. 6 SCIA, Scand. Conf. on Image Analysis, pages , Helsinki, Finland, Suomen Hahmontunnistustutkimuksen seura r.y. [8] Hidemitsu Ogawa, Erkki Oja, and Jouko Lampinen. Projection filters for image and signal restoration. In Proc. IEEE Int. Conf. on Systems Engineering, pages 93 97, Dayton, OH, August [9] Jouko Lampinen and Erkki Oja. Fast computation of Kohonen self-organization. In F. Fogelman-Soulié and J. Herault, editors, Neurocomputing: Algorithms, Architectures, and Applications, NATO ASI Series F: Computer and Systems Sciences, vol. 68, pages Springer, Berlin, Heidelberg, [10] Jouko Lampinen and Erkki Oja. Distortion tolerant feature extraction with Gabor functions and topological coding. In Proc. INNC 90, Int. Neural Network Conf., volume I, pages , Dordrecht, Netherlands, Kluwer. [11] Jouko Lampinen. Feature extractor giving distortion invariant hierarchical feature space. In Steven K. Rogers, editor, Applications of Artificial Neural Networks II, volume 1469, pages ,

2 [12] Jouko Lampinen. Distortion tolerant pattern recognition using invariant transformations and hierarchical SOFM clustering. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, volume II, pages , Amsterdam, Netherlands, North-Holland. [13] Jouko Lampinen. Neural Pattern Recognition: Distortion Tolerance by Self-Organizing Maps. PhD thesis, Lappeenranta University of Technology, Lappeenranta, Finland, [14] Jouko Lampinen and Erkki Oja. Clustering properties of hierarchical self-organizing maps. Journal of Mathematical Imaging and Vision, 2(2-3): , November [15] Jouko Lampinen. On clustering properties of hierarchical self-organizing maps. In I. Aleksander and J. Taylor, editors, Artificial Neural Networks, 2, volume II, pages , Amsterdam, Netherlands, North-Holland. [16] Jouko Lampinen. Experiments on an object recognition system based on gabor filters and hierarchical self-organizing maps. Technical Report 34, Lappeenranta Univ. of Technology, Dept. of Information Technology, [17] Jouko Lampinen and Ossi Taipale. Optimization and simulation of quality properties in paper machine with neural networks. In Proc. ICNN 94, Int. Conf. on Neural Networks, pages , Piscataway, NJ, IEEE Service Center. [18] Erkki Oja and Jouko Lampinen. Feature extraction by unsupervised learning. In T. Ishiguro, editor, Cognitive Processing for Vision and Voice, Proc. Fourth NEC Research Symposium, pages Philadelphia: SIAM, [19] Erkki Oja and Jouko Lampinen. Unsupervised learning for feature extraction. In J. Zurada, R.J. Marks II, and C. Robinson, editors, Computational Intelligence, Imitating Life, pages IEEE Press, New York, [20] Jouko Lampinen, Seppo Ovaska, and Andrew Ugarov. Classification of polynomial-shaped measurement signals using a backpropagation neural network. IEEE Tr. on Instrumentation and Measurement, 43(6), December [21] Jouko Lampinen, Seppo Smolander, Olli Silven, and Hannu Kauppinen. Wood defect recognition: A comparative study. In Proc. of Workshop on Machine Vision in advanced production, Oulu, Finland, [22] Jouko Lampinen and Seppo Smolander. Wood defect recognition with self-organizing feature selection. In D.P. Casasent, editor, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, volume 2353, pages , [23] Jouko Lampinen, Seppo Smolander, and Markku Korhonen. Wood surface inspection system based on generic visual features. In Proceedings of the Industrial Conference "Technical Diagnosis & Nondesctructive Testing" in the International Conference on Artificial Neural Networks, ICANN 95,

3 [24] Jouko Lampinen and Erkki Oja. Distortion tolerant pattern recognition based on selforganizing feature extraction. IEEE Transactions on Neural Networks, 6(3): , May [25] Jouko Lampinen and Seppo Smolander. Fast associative mapping with look-up tables. In F. Fogelman-Soulié and P. Gallinari, editors, Proc. ICANN 95, Int. Conf. on Artificial Neural Networks, volume II, pages , Nanterre, France, EC2. [26] Jouko Lampinen and Arto Selonen. Multilayer perceptron training with inaccurate derivative information. In Proceedings of the IEEE International Conference on Neural Networks ICNN 95, volume 5, pages , Perth, WA, [27] Jouko Lampinen and Seppo Smolander. Self-organizing feature extraction in recognition of wood surface defects and color images. International Journal of Pattern Recognition and Artificial Intelligence, 10(2):97 113, [28] Arto Selonen, Jouko Lampinen, and Leena Ikonen. Using background knowledge in neural network learning. In D.P. Casasent, editor, Intelligent Robots and Computer Vision XV: Algorithms, Techniques, Active Vision, and Materials Handling, volume 2904, pages , [29] Jouko Lampinen. Advances in neural network modeling. In L. Yliniemi and E. Juuso, editors, Proc. of TOOLMET 97, Tool Environments and Development Methods for Intelligent Systems, volume 1, pages 28 36, Plenary presentation. [30] Jouko Lampinen and Arto Selonen. Using background knowledge in multilayer perceptron learning. In M. Frydrych, J. Parkkinen, and A. Visa, editors, Proc. of The 10th Scandinavian Conference on Image Analysis, volume 2, pages , [31] Seppo Smolander and Jouko Lampinen. Determining the optimal structure for multilayer self-organizing map with genetic algorithm. In M. Frydrych, J. Parkkinen, and A. Visa, editors, Proc. of The 10th Scandinavian Conference on Image Analysis, volume 1, pages , [32] Jouko Lampinen, Jorma Laaksonen, and Erkki Oja. Neural network systems, techniques and applications in pattern recognition. Technical Report B1, Helsinki University of Technology, Laboratory of Computational Engineering, February [33] A. Rantala, S. Franssila, K. Kaski, J. Lampinen, M. Åberg, and P. Kuivalainen. Variable threshold MOS-transistor for integrated neural network circuits. In Proc. 15th Norchip Conference, pages 36 43, Tallinn, Technoconsult. [34] Arto Selonen and Jouko Lampinen. Experiments on regularizing MLP models with background knowledge. In W. Gerstner et.al., editor, Artificial Neural Networks - ICANN 97, volume 1327 of Lecture Notes in Computer Science, pages Springer, [35] Jouko Lampinen, Seppo Smolander, and Markku Korhonen. Wood surface inspection system based on generic visual features. In F. Fogelman-Soulié and P. Gallinari, editors, Industrial Applications of Neural Networks, pages World Scientific Pub Co,

4 [36] Jouko Lampinen, editor. Laboratory of Computational Engineering - Annual Report Internal Reports A1. Helsinki University of Technology, Laboratory of Computational Engineering, December [37] Jouko Lampinen. Modeling of non-stationary process by modular separation of stability and plasticity. In Proc IEEE International Joint Conference on Neural Networks, volume 1, pages , Anchorage, Alaska, May [38] Jouko Lampinen, Jorma Laaksonen, and Erkki Oja. Pattern recognition. In C. T. Leondes, editor, Image Processing and Pattern Recognition, volume 5 of Neural Network Systems Techniques and Applications, pages Academic Press, [39] Markus Varsta, Jose del R. Millan, Jukka Heikkonen, and Jouko Lampinen. Temporal sequence processing with the recurrent self-organizing map. In Human and Artificial Information Processing, Proceedings of STeP 98, the 8th finnish artificial intelligence conference, pages , Jyväskylä, Finland, September Picaset Oy, Helsinki. [40] Aki Vehtari, Jouni Juujärvi, Jukka Heikkonen, and Jouko Lampinen. Forest scene classification: Comparison of classifiers. In Human and Artificial Information Processing, Proceedings of STeP 98, the 8th finnish artificial intelligence conference, pages , Jyväskylä, Finland, September Picaset Oy, Helsinki. [41] Markus Varsta, Jukka Heikkonen, José Del Ruiz Millán, and Jouko Lampinen. On the convergence properties of the recurrent self-organizing map. In Proceedings of the ICANN 98, pages , Septemper [42] Aki Vehtari, Jukka Heikkonen, Jouko Lampinen, and Jouni Juujärvi. Using Bayesian neural networks to classify forest scenes. In David P. Casasent, editor, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, volume 3522 of Proceedings of SPIE, pages 66 73, Boston, MA, USA, November [43] Jouni Juujärvi, Jukka Heikkonen, Sami Brandt, and Jouko Lampinen. Digital image based tree measurement for forest industry. In David P. Casasent, editor, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, volume 3522 of Proceedings of SPIE, pages , Boston, MA, November [44] I. Welling, E. Kähkönen, M. Lahtinen, J. Valkonen, J. Lampinen, M. Varsta, and T. Kostiainen. Real time indoor air monitoring system and analysing method. American Journal of Industrial Medicine, Supplement 1:51 54, September [45] Jouko Lampinen, Aki Vehtari, and Kimmo Leinonen. Using Bayesian neural network to solve the inverse problem in electrical impedance tomography. In B. K. Ersboll and P. Johansen, editors, Proceedings of 11th Scandinavian Conference on Image Analysis SCIA 99, pages 87 93, Kangerlussuaq, Greenland, June [46] Aki Vehtari and Jouko Lampinen. Bayesian neural networks for image analysis. In B. K. Ersboll and P. Johansen, editors, Proceedings of 11th Scandinavian Conference on Image Analysis SCIA 99, pages , Kangerlussuaq, Greenland, June

5 [47] Aki Vehtari and Jouko Lampinen. Bayesian neural networks with correlating residuals. In Proc. IJCNN 99, Washington, DC, USA, July [48] Jouko Lampinen, Aki Vehtari, and Kimmo Leinonen. Application of Bayesian neural network in electrical impedance tomography. In Proc. IJCNN 99, Washington, DC, USA, July [49] Jouko Lampinen, Paula Litkey, and Harri Hakkarainen. Selection of training samples for learning with hints. In Proc. IJCNN 99, Washington, DC, USA, July [50] Jouko Lampinen and Timo Kostiainen. Overtraining and model selection with the selforganizing map. In Proc. IJCNN 99, Washington, DC, USA, July [51] Jukka Heikkonen and Jouko Lampinen. Building industrial applications with neural networks. In Proc. European Symposium on Intelligent Techniques, ESIT 99, Chania, Greece, June [52] Aki Vehtari and Jouko Lampinen. Bayesian neural networks for industrial applications. In Proceedings of SMCIA/ IEEE Midhight-Sun Workshop on Soft Computing Methods in Industrial Applications, pages 63 68, Kuusamo, Finland, June [53] A. Rantala, S. Franssila, K. Kaski, J. Lampinen, M. Aberg, and P. Kuivalainen. Highprecision neuron MOSFET structures. Electronics Letters, 35(2): , [54] Jouko Lampinen and Kimmo Kaski, editors. Laboratory of Computational Engineering - Bi-Annual Report Internal Reports A2. Helsinki University of Technology, Laboratory of Computational Engineering, December [55] Aki Vehtari and Jouko Lampinen. Bayesian neural networks: Case studies in industrial applications. In Y. Suzuki, R. Roy, S. J. Ovaska, T. Furuhashi, and Y. Dote, editors, Soft Computing in Industrial Applications, pages Springer-Verlag, [56] Aki Vehtari and Jouko Lampinen. Bayesian MLP neural networks - review and case studies. In Leena Yliniemi and Esko Juuso, editors, Proceedings of TOOLMET2000, Tool Environments and Development Methods for Intelligent Systems, pages , Oulu, Finland, April Oulun Yliopistopaino. [57] Jouko Lampinen and Timo Kostiainen. Self-Organizing Map in data-analysis - notes on overfitting and overinterpretation. In Michel Verleysen, editor, Proc. ESANN 2000, pages , Bruges, Belgium, April D-Facto. [58] Markus Varsta, Jukka Heikkonen, and Jouko Lampinen. Analytical comparison of the Temporal Kohonen Map and the Recurrent Self Organizing Map. In Michel Verleysen, editor, Proc. ESANN 2000, pages , Bruges, Belgium, April D-Facto. [59] Irma Welling, Erkki Kähkönen, Marjaana Lahtinen, Kari Salmi, Jouko Lampinen, and Timo Kostiainen. Modelling of occupants subjective responses and indoor air quality in office buildings. In Proceedings of the Ventilation 2000, 6th International Symposium on Ventilation for Contaminant Control, volume 2, pages 45 49, Helsinki, Finland, June

6 [60] Jani Lahtinen, Tomas Martinsen, and Jouko Lampinen. Improved rotational invariance for statistical inverse in electrical impedance tomography. In Shun-Ichi Amari, C. Lee Giles, Marco Gori, and Vincenzo Piuri, editors, Proceedings of the IJCNN 2000, volume II, pages , Como, Italy, July IEEE Computer Society. [61] Aki Vehtari, Simo Särkkä, and Jouko Lampinen. On MCMC sampling in Bayesian MLP neural networks. In Shun-Ichi Amari, C. Lee Giles, Marco Gori, and Vincenzo Piuri, editors, Proceedings of the IJCNN 2000, volume I, pages , Como, Italy, July IEEE Computer Society. [62] Jouko Lampinen and Aki Vehtari. Bayesian techniques for neural networks - review and case studies. In M. Gabbouj and P. Kuosmanen, editors, Proceedings of Eusipco 2000, X European Signal Processing Conference, volume 2, pages , Tampere, Finland, September [63] Timo Kostiainen and Jouko Lampinen. Maximum likelihood optimization of selforganizing map parameters. In Proceedings of SCI 2000, 4th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, USA, July [64] Aki Vehtari and Jouko Lampinen. Bayesian MLP neural networks for image analysis. Pattern Recognition Letters, 21(13 14): , December [65] A. Rantala, S. Franssila, K. Kaski, J. Lampinen, and P. Kuivalainen. Improved neuron MOS-transistor structures for integrated neural network circuits. IEEE Proc.-Circuits Devices Syst., 148(1):25 34, February [66] Aki Vehtari and Jouko Lampinen. On Bayesian model assessment and choice using crossvalidation predictive densities. Technical Report B23, Helsinki University of Technology, Laboratory of Computational Engineering, April [67] Jouko Lampinen and Aki Vehtari. Bayesian approach for neural networks review and case studies. Neural Networks, 14(3):7 24, April (Invited article). [68] Markus Varsta, Jukka Heikkonen, Jouko Lampinen, and José del R. Millán. Temporal kohonen map and the recurrent self-organizing map: Analytical and experimental comparison. Neural Processing Letters, 13(3): , June [69] Timo Kostiainen and Jouko Lampinen. Self-organizing map as a probability density model. In Proceedings of IJCNN 2001, Washington, D.C, USA, July [70] Aki Vehtari and Jouko Lampinen. Bayesian model assessment and comparison using crossvalidation predictive densities. Technical Report B27, Laboratory of Computational Engineering, Helsinki University of Technology, September [71] Jouko Lampinen and Aki Vehtari. Applied Computational Intelligence to Engineering and Business, chapter Bayesian Techniques for Neural Networks Review and Case Studies. Lecture notes of the Nordic, Baltic and Northwest Russian Summer School, NBR RIGA Technical University,

7 [72] Aki Vehtari and Jouko Lampinen. Bayesian input variable selection using cross-validation predictive densities and reversible jump MCMC. Technical Report B28, Laboratory of Computational Engineering, Helsinki University of Technology, September [73] Tobias Andersen, Kaisa Tiippana, Jouko Lampinen, and Mikko Sams. Modeling of audiovisual speech perception in noise. In Dominic Massaro, Joanna Light, and Kristin Geraci, editors, Proc. of the 4th international conference on auditory-visual speech processing, AVSP 2001, volume 4572, pages , Aalborg, Denmark, September [74] Jouko Lampinen, Toni Tamminen, Timo Kostiainen, and Ilkka Kalliomäki. Bayesian object matching based on MCMC sampling and gabor filters. In D.P. Casasent and E.L. Hall, editors, Proc. SPIE Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, volume 4572, pages 41 50, Newton, MA, October [75] Timo Kostiainen, Ilkka Kalliomäki, Toni Tamminen, and Jouko Lampinen. 3D object recognition based on hierarchical eigen-shapes and bayesian inference. In D.P. Casasent and E.L. Hall, editors, Proc. SPIE Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, volume 4572, pages , Newton, MA, October [76] Aki Vehtari and Jouko Lampinen. Bayesian input variable selection using posterior probabilities and expected utilities. Technical Report B31, Helsinki University of Technology, Laboratory of Computational Engineering, December [77] Jouko Lampinen and Timo Kostiainen. Generative probability density model in the Self- Organizing Map. In U. Seiffert and L. Jain, editors, Self-organizing neural networks: Recent advances and applications, pages Physica Verlag, [78] Aki Vehtari and Jouko Lampinen. Bayesian model assessment and comparison using crossvalidation predictive densities. Neural Computation, 14(10): , [79] Jouko Lampinen and Aki Vehtari. Neljännesvuosisata Hatutusta: Hahmontunnistustutkimus Suomessa , chapter Bayesilaiset menetelmät hahmontunnistuksessa (in Finnish), pages Suomen hahmontunnistustutkimuksen seura ry, [80] Toni Tamminen and Jouko Lampinen. Face matching with learned object priors. In P. Ala- Siuru and S. Kaski, editors, STeP Intelligence, The Art of Natural and Artificial. Proc. 10th Finnish Artificial Intelligence Society, [81] Timo Kostiainen and Jouko Lampinen. On the generative probability density model in the Self-Organizing Map. Neurocomputing, 48: , October [82] Tommi Orpana and Jouko Lampinen. Building spacial models from aggregate data. Journal of Regional Science, 43(2): , May [83] Aki Vehtari and Jouko Lampinen. Expected utility estimation via cross-validation. In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West, editors, Bayesian Statistics 7, pages Oxford University Press,

8 [84] Toni Tamminen and Jouko Lampinen. Bayesian object matching with hierarchical priors and Markov chain Monte Carlo. In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West, editors, Bayesian Statistics 7, pages Oxford University Press, [85] Toni Tamminen and Jouko Lampinen. Learning an object model for feature matching in clutter. In J. Bigun and T. Gustavsson, editors, Proceedings of SCIA 2003, volume 2749 of Lecture Notes in Computer Science, pages Springer, [86] Ilkka Kalliomäki and Jouko Lampinen. Modeling of pose effects in oriented filter responses for head pose estimation. In J. Bigun and T. Gustavsson, editors, Proceedings of SCIA 2003, volume 2749 of Lecture Notes in Computer Science, pages Springer, [87] Jani Lahtinen and Jouko Lampinen. Reversible jump MCMC for two state multivariate Poisson mixtures. Kybernetika, 99(3): , [88] Toni Tamminen and Jouko Lampinen. A Bayesian occlusion model for sequential object matching. In A. Hoppe, S. Barman, and T. Ellis, editors, Proc. British Machine Vision Conference 2004, volume 2, pages , The paper was awarded the Model- Based Vision Prize BMVC [89] Timo Kostiainen and Jouko Lampinen. Efficient proposal distributions for MCMC image segmentation. In Proc. IEEE International Conference on Image Processing, ICIP 2004, Singapore, October [90] Timo Kostiainen and Jouko Lampinen. Probabilistic image segmentation for low-level map building in robot navigation. In Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004), Oulu, Finland, June [91] Aki Vehtari and Jouko Lampinen. Model selection via predictive explanatory power. Technical Report B38, Helsinki University of Technology, Laboratory of Computational Engineering, July [92] Simo Särkkä, Aki Vehtari, and Jouko Lampinen. Rao-Blackwellized Monte Carlo data association for multiple target tracking. In Per Svensson and Johan Schubert, editors, Proceedings of the Seventh International Conference on Information Fusion, volume I, pages , Mountain View, CA, Jun International Society of Information Fusion. [93] Simo Särkkä, Aki Vehtari, and Jouko Lampinen. Time series prediction by Kalman smoother with cross-validated noise density. In IJCNN 2004: Proceedings of the 2004 International Joint Conference on Neural Networks, The Winner of Time Series Prediction Competition - The CATS Benchmark. [94] Simo Särkkä, Toni Tamminen, Aki Vehtari, and Jouko Lampinen. Probabilistic methods in multiple target tracking - review and bibliography. Technical Report B36, Helsinki University of Technology, Laboratory of Computational Engineering,

9 [95] Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari, and Mikko Sams. Bayesian analysis of the neuromagnetic inverse problem with l p -norm priors. NeuroImage, 26: , [96] Ilkka Kalliomäki and Jouko Lampinen. Approximate steerability of gabor filters for feature detection. In Image Analysis: 14th Scandinavian Conference, SCIA 2005, pages , [97] Toni Tamminen, Jari Kätsyri, Michael Frydrych, and Jouko Lampinen. Joint modeling of facial expression and shape from video. In Image Analysis: 14th Scandinavian Conference, SCIA 2005, pages , [98] Ilkka Kalliomäki, Aki Vehtari, and Jouko Lampinen. Shape analysis of concrete aggregates for statistical quality modeling. Machine Vision and Applications, 16(3): , URL: [99] S. Särkkä, A. Vehtari, and J. Lampinen. CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing, 70(13-15): , [100] S. Särkkä, A. Vehtari, and J. Lampinen. Rao-Blackwellized particle filter for multiple target tracking. Information Fusion, 8(1):2 15, [101] Toni Tamminen and Jouko Lampinen. Sequential Monte Carlo for Bayesian matching of objects with occlusions. IEEE Tr. on Pattern Analysis and Machine Intelligence, 28(6), [102] A. Nummenmaa, T. Auranen, M.S. Hämäläinen, I.P. Jääskeläinen, J. Lampinen, M. Sams, and A. Vehtari. Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods. Neuroimage, 35(2): , [103] A. Nummenmaa, T. Auranen, M.S. Hämäläinen, I.P. Jääskeläinen, M. Sams, A. Vehtari, and J. Lampinen. Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. Neuroimage, 37(3): , [104] I. Kalliomäki and J. Lampinen. On steerability of Gabor-type filters for feature detection. Pattern Recognition Letters, 28(8): , [105] T. Auranen, A. Nummenmaa, MS Hamalainen, IP Jaaskelainen, J. Lampinen, A. Vehtari, and M. Sams. Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles. Hum Brain Mapp, 28(10): , [106] Irma Welling, Timo Kostiainen, Marjaana Lahtinen, Kari Salmi, Erkki Kähkönen, and Jouko Lampinen. Modelling of subjective responses to indoor air quality and thermal conditions in office buildings. HVAC & R Research, 14(6): , Nov

10 [107] T. Auranen, A. Nummenmaa, S. Vanni, A. Vehtari, M.S. Hämäläinen, J. Lampinen, and I.P. Jääskeläinen. Automatic fmri-guided MEG multidipole localization for visual responses. Hum Brain Mapp, 30: , [108] M. Toivanen and J. Lampinen. Incremental Bayesian learning of feature points from natural images. In Proc. CVPR, pages 39 46, [109] M. Toivanen and J. Lampinen. Bayesian Online Learning of Corresponding Points of Objects with Sequential Monte Carlo. International Journal of Computational Intelligence, 5(4),