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Publications

[1]   M. Cwiek and J. Nalepa, “A fast genetic algorithm for the flexible job shop scheduling problem,” in Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion, New York, NY, USA, 2014, GECCO Comp ’14, pp. 1449–1450, ACM.

[2]   U. Boryczka and K. Dworak, “Cryptanalysis of transposition cipher using evolutionary algorithms,” in Computational Collective Intelligence. Technologies and Applications, H. Dosam, J. J. Jason, and N. T. Nguyen, Eds., vol. 8733 of Lecture Notes in Computer Science, pp. 623–632. Springer International Publishing, 2014.

[3]   U. Boryczka and K. Dworak, “Genetic transformation techniques in cryptanalysis,” in Intelligent Information and Database Systems, N. T. Nguyen, B. Attachoo, B. Trawinski, and K. Somboonviwat, Eds., vol. 8398 of Lecture Notes in Computer Science, pp. 147–156. Springer International Publishing, 2014.

[4]   K. Harezlak, P. Kasprowski, and M. Stasch, “Idiosyncratic repeatability of calibration errors during eye tracker calibration,” in 7th International Conferenceon Human System Interactions (HSI), June 2014, pp. 95–100.

[5]   H. Josiński, A. Michalczuk, D. Kostrzewa, A. Świtoński, and K. Wojciechowski, “Heuristic method of feature selection for person re-identification based on gait motion capture data,” in Intelligent Information and Database Systems, pp. 585–594. Springer International Publishing, 2014.

[6]   H. Josiński, D. Kostrzewa, A. Michalczuk, and A. Świtoński, “The expanded invasive weed optimization metaheuristic for solving continuous and discrete optimization problems,” The Scientific World Journal, vol. 2014, 2014.

[7]   P. Kasprowski, K. Harężlak, and M. Stasch, “Guidelines for the eye tracker calibration using points of regard,” in Information Technologies in Biomedicine, Volume 4, E. Piętka, J. Kawa, and W. Wieclawek, Eds., vol. 284 of Advances in Intelligent Systems and Computing, pp. 225–236. Springer International Publishing, 2014.

[8]   M. Kawulok, J. Nalepa, and J. Kawulok, “Skin detection and segmentation in color images,” in Advances in Low-Level Color Image Processing, M. E. Celebi and B. Smolka, Eds., vol. 11 of Lecture Notes in Computational Vision and Biomechanics, pp. 329–366. Springer Netherlands, 2014.

[9]   M. Kawulok and J. Nalepa, “Hand pose estimation using support vector machines with evolutionary training,” in Proc. International Conference on Systems, Signals and Image Processing (IWSSIP 2014), May 2014, pp. 87–90.

[10]   M. Kawulok, J. Kawulok, and J. Nalepa, “Spatial-based skin detection using discriminative skin-presence features,” Pattern Recognition Letters, vol. 41, pp. 3–13, 2014.

[11]   D. Kostrzewa and H. Josiński, “Evaluation of the exiwo algorithm based on the traveling salesman problem,” International Journal of Mathematical, Computational, Physical and Quantum Engineering, vol. 9, no. 8, pp. 1114, 2014.

[12]   D. Kostrzewa and H. Josiński, “The exIWO metaheuristic–a recapitulation of the research on the join ordering problem,” in Beyond Databases, Architectures, and Structures, pp. 10–19. Springer International Publishing, 2014.

[13]   J. Nalepa and M. Kawulok, “Fast and accurate hand shape classification,” in Beyond Databases, Architectures, and Structures, S. Kozielski, D. Mrozek, P. Kasprowski, B. Malysiak-Mrozek, and D. Kostrzewa, Eds., vol. 424 of Communications in Computer and Information Science, pp. 364–373. Springer, 2014.

[14]   J. Nalepa, J. Szymanek, M. Hayball, S. Brown, B. Ganeshan, and K. Miles, “Texture analysis for identifying heterogeneity in medical images,” in Computer Vision and Graphics, L. Chmielewski, R. Kozera, B.-S. Shin, and K. Wojciechowski, Eds., vol. 8671 of Lecture Notes in Computer Science, pp. 446–453. Springer International Publishing, 2014.

[15]   J. Nalepa and M. Kawulok, “A memetic algorithm to select training data for support vector machines,” in Proc. Conference on Genetic and Evolutionary Computation (GECCO ’14). 2014, pp. 573–580, ACM.

[16]   J. Nalepa, “Adaptive memetic algorithm for the vehicle routing problem with time windows,” in Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion, New York, NY, USA, 2014, GECCO Comp ’14, pp. 1467–1468, ACM.

[17]   J. Nalepa, T. Grzejszczak, and M. Kawulok, “Wrist localization in color images for hand gesture recognition,” in Man-Machine Interactions 3, D. A. Gruca, T. Czachórski, and S. Kozielski, Eds., vol. 242 of Advances in Intelligent Systems and Computing, pp. 79–86. Springer International Publishing, 2014.

[18]   J. Nalepa, M. Blocho, and Z. Czech, “Co-operation schemes for the parallel memetic algorithm,” in Parallel Processing and Applied Mathematics, R. Wyrzykowski, J. Dongarra, K. Karczewski, and J. Waśniewski, Eds., Lecture Notes in Computer Science, pp. 191–201. Springer Berlin Heidelberg, 2014.

[19]   K. Pawelczyk and M. Kawulok, “Head pose estimation relying on appearance-based nose region analysis,” in Computer Vision and Graphics, L. Chmielewski, R. Kozera, B.-S. Shin, and K. Wojciechowski, Eds., vol. 8671 of Lecture Notes in Computer Science, pp. 510–517. Springer International Publishing, 2014.

[20]   Ł. Stypka and M. Kozielski, “Methods of gene ontology term similarity analysis in graph database environment,” in Beyond Databases, Architectures, and Structures, S. Kozielski, D. Mrozek, P. Kasprowski, B. Małysiak-Mrozek, and D. Kostrzewa, Eds., vol. 424 of Communications in Computer and Information Science, pp. 345–354. Springer International Publishing, 2014.

[21]   Ł. Stypka and M. Kozielski, “Methods of normalization the results of gene ontology term similarity,” Studia Informatica, vol. 35, no. 2, pp. 7–18, 2014.

[22]   T. Grzejszczak, J. Nalepa, and M. Kawulok, “Real-time wrist localization in hand silhouettes,” in Proc. 8th International Conference on Computer Recognition Systems CORES 2013, R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, and A. Zolnierek, Eds., vol. 226 of Advances in Intelligent Systems and Computing, pp. 439–449. Springer International Publishing, 2013.

[23]   H. Josiński, A. Świtoński, K. Jędrasiak, and D. Kostrzewa, “Human identification based on tensor representation of the gait motion capture data,” in IAENG Transactions on Electrical Engineering: Special Issue of the International Multiconference of Engineers and Computer Scientists 2012, pp. 111–122. World Scientific Publishing Company, 2013.

[24]   H. Josiński, A. Świtoński, A. Michalczuk, D. Kostrzewa, and K. Wojciechowski, “Human identification based on gait video sequences,” in International Conference on Computer Science and Engineering. WASET, 2013, pp. 312–317.

[25]   H. Josiński, D. Kostrzewa, A. Michalczuk, A. Świtoński, and K. Wojciechowski, “Feature extraction and HMM-based classification of gait video sequences for the purpose of human identification,” in Vision Based Systems for UAV Applications, pp. 233–245. Springer International Publishing, 2013.

[26]   M. Kawulok, “Fast propagation-based skin regions segmentation in color images,” in Proc. IEEE International Conference on Automatic Face and Gesture Recognition, 2013, pp. 1–7.

[27]   M. Kawulok, J. Kawulok, J. Nalepa, and M. Papiez, “Skin detection using spatial analysis with adaptive seed,” in Proc. IEEE International Conference on Image Processing (ICIP 2013), Sept 2013, pp. 3720–3724.

[28]   D. Kostrzewa and J. Henryk, “Using the expanded IWO algorithm to solve the traveling salesman problem,” in 5th International Conference on Agents and Artificial Intelligence. SCITEPRESS – Science and Technology Publications, 2013, pp. 451–456.

[29]   M. Kozielski and Ł. Stypka, “Gene ontology based gene analysis in graph database environment,” Studia Informatica, vol. 34, no. 2A, pp. 111, 2013.

[30]   J. Nalepa and Z. J. Czech, “New selection schemes in a memetic algorithm for the vehicle routing problem with time windows,” in Adaptive and Natural Computing Algorithms, M. Tomassini, A. Antonioni, F. Daolio, and P. Buesser, Eds., vol. 7824 of Lecture Notes in Computer Science, pp. 396–405. Springer Berlin Heidelberg, 2013.

[31]   J. Nalepa and M. Kawulok, “Parallel hand shape classification,” in Proc. IEEE International Symposium on Multimedia (ISM 2013), 2013, pp. 401–402.

[32]   M. Papiez and M. Kawulok, “Adaptive skin detection in colour images using error signal space,” Studia Informatica, vol. 34, no. 2A, pp. 365–377, 2013.

[33]    K. A. Cyran, J. Kawulok, M. Kawulok, M. Stawarz, M. Michalak, M. Pietrowska, P. Widlak, and J. Polanska, “Support vector machines in biomedical and biometrical applications,” in Emerging Paradigms in Machine Learning, S. Ramanna, L. C. Jain, and R. J. Howlett, Eds., vol. 13 ofSmart Innovation, Systems and Technologies, pp. 379–417. Springer Berlin Heidelberg, 2013.

[34]   D. Augustyn and D. Kostrzewa, “Selectivity estimation of inquiries based on Hough transform and PCA method [Szacowanie selektywności zapytań oparte na transformacie Hougha i metodzie PCA],” Studia Informatica, vol. 33, no. 2A, pp. 105, 2012.

[35]   M. Czupryna and M. Kawulok, “Real-time vision pointer interface,” in Proc. International Symposium ELMAR 2012, 2012, pp. 49–52.

[36]   H. Josiński, A. Świtoński, K. Jędrasiak, and D. Kostrzewa, “Human identification based on gait motion capture data,” Lecture Notes in Engineering and Computer Science, vol. 2195, pp. 507–510, 2012.

[37]   M. Kawulok, “Skin region detection in digital images using discriminative textural features,” Studia Informatica, vol. 33, no. 2B, pp. 37–48, 2012.

[38]   M. Kawulok, “Skin detection using color and distance transform,” in Computer Vision and Graphics, L. Bolc, R. Tadeusiewicz, L. Chmielewski, and K. Wojciechowski, Eds., vol. 7594 of Lecture Notes in Computer Science, pp. 449–456. Springer Berlin Heidelberg, 2012.

[39]   M. Kawulok and J. Szymanek, “Precise multi-level face detector for advanced analysis of facial images,” Image Processing, IET, vol. 6, no. 2, pp. 95–103, 2012.

[40]   M. Kawulok, “Texture analysis for skin probability maps refinement,” in Pattern Recognition, J. A. Carrasco-Ochoa, J. F. Martinez-Trinidad, J. A. Olvera Lopez, and K. L. Boyer, Eds., vol. 7329 of Lecture Notes in Computer Science, pp. 75–84. Springer Berlin Heidelberg, 2012.

[41]   M. Kawulok and J. Nalepa, “Support vector machines training data selection using a genetic algorithm,” in Structural, Syntactic, and Statistical Pattern Recognition, G. Gimel’farb, E. Hancock, A. Imiya, A. Kuijper, M. Kudo, S. Omachi, T. Windeatt, and K. Yamada, Eds., vol. 7626 ofLecture Notes in Computer Science, pp. 557–565. Springer Berlin Heidelberg, 2012.

[42]   D. Kostrzewa and H. Josiński, “The modified IWO algorithm for optimization of numerical functions,” in Swarm and Evolutionary Computation, pp. 267–274. Springer Berlin Heidelberg, 2012.

[43]   G. Koszowski and M. Kawulok, “Virtual hand modeling for gesture recognition,” Studia Informatica, vol. 33, no. 2B, pp. 35–36, 2012.

[44]   J. Nalepa and Z. J. Czech, “A parallel heuristic algorithm to solve the vehicle routing problem with time windows,” Studia Informatica, vol. 33, no. 1, pp. 91–106, 2012.

[45]   J. Nalepa and Z. J. Czech, “Adaptive threads co-operation schemes in a parallel heuristic algorithm for the vehicle routing problem with time windows,” Theoretical and Applied Informatics, vol. 24, no. 3, pp. 191–203, 2012.

[46]   M. Kawulok, J. Kawulok, and B. Smolka, “Discriminative textural features for image and video colorization,” IEICE Transactions on Information and Systems, vol. 95-D, no. 7, pp. 1722–1730, 2012.

[47]   L. Jarosinski and M. Kawulok, “Application of multiscale analysis for human skin detection in digital images [Zastosowanie analizy wieloskalowej do detekcji ludzkiej skóry w obrazach cyfrowych],” Studia Informatica, vol. 32, no. 2A, pp. 565–578, 2011.

[48]   M. Kawulok, J. Kawulok, and B. Smolka, “Textural features for scribble-based image colorization,” in Computer Recognition Systems 4, R. Burduk, M. Kurzynski, M. Wozniak, and A. Zolnierek, Eds., vol. 95 of Advances in Intelligent and Soft Computing, pp. 269–278. Springer Berlin Heidelberg, 2011.

[49]   M. Kawulok and B. Smolka, “Texture-adaptive image colorization framework,” EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 99, 2011.

[50]   M. Kawulok, J. Kawulok, and B. Smolka, “Image colorization using discriminative textural features,” in Proc. IAPR Conference on Machine Vision Applications MVA 2011, Nara, Japan, 2011, pp. 198–201.

[51]   M. Kawulok, J. Wu, and E. R. Hancock, “Supervised relevance maps for increasing the distinctiveness of facial images,” Pattern Recognition, vol. 44, no. 4, pp. 929–939, 2011.

[52]   D. Kostrzewa and H. Josiński, Verification of the search space exploration strategy based on the solutions of the join ordering problem [Ocena jakości strategii eksploracji przestrzeni poszukiwań dla problemu określenia kolejności realizacji złączeń],” Studia Informatica, vol. 32, no. 2A, pp. 37–46, 2011.

[53]   D. Kostrzewa and H. Josiński, “Verification of the search space exploration strategy based on the solutions of the join ordering problem,” in Man-Machine Interactions 2, pp. 447–455. Springer Berlin Heidelberg, 2011.

[54]   M. Kawulok and B. Smolka, “Image colorization with competitive propagation paths and chrominance blending,” Studia Informatica, vol. 31, no. 2A, pp. 321–333, 2010.

[55]   M. Kawulok and B. Smolka, “Competitive image colorization,” in Proc. IEEE International Conference on Image Processing (ICIP 2010), 2010, pp. 405–408.

[56]   M. Kawulok and B. Smolka, “Image colorization system based on discriminating textural features,” in Advances in Systems Science, pp. 323–332. Academic Publishing House EXIT, Warsaw, 2010.

[57]   M. Kawulok, “Energy-based blob analysis for improving precision of skin segmentation,” Multimedia Tools and Applications, vol. 49, no. 3, pp. 463–481, 2010.

[58]   D. Kostrzewa and H. Josiński, Methods for search of the space of query execution plans by means of the IWO algorithm [Metody przeszukiwania przestrzeni planów realizacji zapytań za pomocą algorytmu IWO],” Studia Informatica, vol. 31, no. 2A, pp. 393–403, 2010.

[59]   D. Kostrzewa and H. Josiński, Application of IWO algorithm for solving the travelling salesman problem [Zastosowanie algorytmu IWO do rozwiązania problemu komiwojażera],” in III Krajowa Konferencja Naukowa Technologie Przetwarzania Danych. WNT, 2010, pp. 238–247.

[60]   M. Kawulok, “Adaptive skin detector enhanced with blob analysis for gesture recognition,” in Proc. International Symposium ELMAR ’09, 2009, pp. 37–40.

[61]   J. Wu, W. A. P. Smith, E. R. Hancock, and M. Kawulok, “Extracting gender discriminating features from facial needle-maps,” in Proc. IEEE International Conference on Image Processing (ICIP 2009), 2009, pp. 2449–2452.

[62]   D. Kostrzewa and H. Josiński, Application of IWO algorithm for planning of distributed data merging process [Zastosowanie algorytmu IWO do planowania procesu scalania danych rozproszonych],” Studia Informatica, vol. 30, no. 2A, pp. 83, 2009.

[63]   D. Kostrzewa and H. Josiński, “The comparison of an adapted evolutionary algorithm with the invasive weed optimization algorithm based on the problem of predetermining the progress of distributed data merging process,” in Man-Machine Interactions, pp. 505–514. Springer Berlin Heidelberg, 2009.

[64]   M. Kawulok and J. Szymanek, “Algorithm for precise frontal face detection,” Studia Informatica, vol. 30, pp. 341–354, 2009.

[65]   M. Kawulok, Adaptive skin detector in digital images, [Adaptacyjny detektor skóry w obrazach cyfrowych],” in Databases. Development of methods and methodologies. Collective Work. [T. 1]: Architecture, formal methods and advanced data analysis, pp. 503–514. WKiŁ, Warsaw, 2008.

[66]   M. Kawulok, “Optimization of face relevance maps with total classification error minimization,” in Image Analysis and Recognition, A. Campilho and M. Kamel, Eds. 2008, vol. 5112 of Lecture Notes in Computer Science, pp. 935–944, Springer Berlin Heidelberg.

[67]   M. Kawulok, “Dynamic skin detection in color images for sign language recognition,” in Image and Signal Processing, A. Elmoataz, O. Lezoray, F. Nouboud, and D. Mammass, Eds., vol. 5099 of Lecture Notes in Computer Science, pp. 112–119. Springer Berlin Heidelberg, 2008.

[68]   D. Kostrzewa and H. Josiński, “Planning of the process of distributed data merging by means of evolutionary algorithm, [Planowanie procesu scalania danych rozproszonych za pomocą algorytmu ewolucyjnego],” Architecture, Formal Methods and Advanced Data Analysis, pp. 13–26, 2008.

[69]   M. Kawulok, “Genetic algorithms for training sets optimization applied in the area of face recognition,” in Recent Developments in Artificial Intelligence Methods, pp. 85–93. AI-METH Series, 2007.

[70]   M. Kawulok, “Genetic algorithms for classifiers’ training sets optimization applied to human face recognition,” Journal of Medical Informatics & Technologies, vol. 11, pp. 135–143, 2007.

[71]   A. Duszenko and M. Kawulok, Automatic face recognition as a key element of multimedia databases, [Automatyczne rozpoznawanie twarzy jako istotny element multimedialnych baz danych],” in Databases. Structures, algorithms, methods: Selected technologies and their applications, S. Kozielski, Ed., vol. 2, pp. 377–384. WKiŁ, Warsaw, 2006.

[72]   M. Kawulok and B. Smolka, “Improvement of face recognition effectiveness based on color information,” in Proc. 13th International Workshop on Systems, Signals and Image Processing (IWSSIP 2006), 2006, pp. 69–73.

[73]   M. Kawulok and B. Smolka, “Application of color information in human face recognition,” in Proc. 11th International Conference on Medical Informatics and Technology, MIT 2006, 2006, pp. 395–400.

[74]   M. Kawulok, “Masks and eigenvectors weights for eigenfaces method improvement,” in Computer Vision and Graphics, K. Wojciechowski, B. Smolka, H. Palus, R. Kozera, W. Skarbek, and L. Noakes, Eds., vol. 32 of Computational Imaging and Vision, pp. 522–527. Springer Netherlands, 2006.

[75]   M. Kawulok and S. Kozielski, “Use of database applications for effective management of large buildings, [Wykorzystanie aplikacji bazodanowych do efektywnego zarządzania dużymi budynkami],” in Databases. Models, technologies, tools: Data analysis and selected applications, S. Kozielski, Ed., vol. 2, pp. 301–308. WKiŁ, Warsaw, 2005.

[76]   M. Kawulok, “Application of support vector machines in automatic human face recognition,” Journal of Medical Informatics & Technologies, vol. 9, pp. 143–150, 2005.

[77]   A. Momot and M. Kawulok, “Sparse Bayesian learning in classifying face feature vectors,” Journal of Medical Informatics & Technologies, vol. 9, pp. 151–158, 2005.

[78] J. Nalepa, M. Kawulok, “Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs”, in Neurocomputing, Elsevier, 2015

[79] J. Nalepa, J. Szymanek and M. Kawulok, “Real-Time People Counting from Depth Images” in Beyond Databases, Architectures and Structures, S. Kozielski et al. (Eds.), vol. 521 of the series Communications in Computer and Information Science, pp. 387-397, Springer International Publishing, 2015

[80] J. Nalepa, M. Cwiek and M. Kawulok, “Adaptive memetic algorithm for the job shop scheduling problem”, in Neural Networks (IJCNN), 2015 International Joint Conference on, July 2015, pp. 1-8

[81] T. Grzejszczak, M. Kawulok, A. Gałuszka, “Hand landmarks detection and localization in color images”, in Multimedia Tools and Applications, pp. 1-25, Springer International Publishing, 2015

[82] K. Radlak, M. Frackiewicz, M. Szczepanski, M. Kawulok, M. Czardybon, “Adaptive Vision Studio — Educational tool for image processing learning”, in Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE, pp. 1-8

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