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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Toward adaptive heuristic video frames capturing and correction in real-time

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DOI: http://dx.doi.org/10.15439/2016F143

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 849852 ()

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Abstract. Multimedia devices are widely used in professional applications as well as personal purposes. The use of computer vision systems enables detection and extraction of important features exposed in images. However constantly increasing demand for this type of video with high quality requires simple however reliable methods. The objective of presented research is to investigate applicability of heuristic method for real-time video frames capturing and correction.

References

  1. A. Pope and D. Lowe, “Probabilistic models of appearance for 3-d object recognition,” International Journal of Computer Vision, vol. 40, no. 2, pp. 149–167, 1998.
  2. R. Grycuk, M. Knop, and S. Mandal, “Video key frame detection based on SURF algorithm,” Lecture Notes in Artificial Intelligence - ICAISC’2015, vol. 9119, pp. 566–576, 2015, http://dx.doi.org/10.1007/978-3-319-19324-3.
  3. P. Drozda, K. Sopyla, and P. Górecki, “Different orderings and visual sequence alignment algorithms for image classification,” Lecture Notes in Artificial Intelligence - ICAISC’2014, vol. 8467, pp. 693–702, 2014, http://dx.doi.org/10.1007/978-3-319-07173-2.
  4. M. Knop, T. Kapuscinski, W. K. Mleczko, and R. A. Angryk, “Neural video compression based on RBM scene change detection algorithm,” Lecture Notes in Artificial Intelligence - ICAISC’2016, vol. 9693, pp. 660–669, 2016, http://dx.doi.org/10.1007/978-3-319-39384-1_58.
  5. G. Capizzi, G. Lo Sciuto, C. Napoli, E. Tramontana, and M. Woźniak, “Automatic classification of the fruit defects based on co-occurrence matrix and neural networks,” in Proceedings of the Federated Conference on Computer Science and Information Systems - FedCSIS’2015. 13-16 September, Lodz, Poland: IEEE, 2015, pp. 861–867, http://dx.doi.org/10.15439/2015F258.
  6. A. Stateczny, M. Wlodarczyk-Sielicka, and G. Zaniewicz, “Different orderings and visual sequence alignment algorithms for image classification,” Annual of Navigation, vol. 19, no. 2, pp. 99–108, 2012, http://dx.doi.org/10.2478/v10367-012-0020-x.
  7. J. A. Starzyk, “Visual saccades for object recognition,” Lecture Notes in Artificial Intelligence - ICAISC’2015, vol. 9119, pp. 778–788, 2015, http://dx.doi.org/10.1007/978-3-319-19324-3_70.
  8. S. Pabiasz, J. T. Starczewski, and A. Marvuglia, “A new three-dimensional facial landmarks in recognition,” Lecture Notes in Artificial Intelligence - ICAISC’2014, vol. 8468, pp. 179–186, 2014, http://dx.doi.org/10.1007/978-3-319-07176-3_16.
  9. S. Pabiasz, J. T. Starczewski, and A. Marvuglia, “SOM vs FCM vs PCA in 3d face recognition,” Lecture Notes in Artificial Intelligence - ICAISC’2015, vol. 9120, pp. 120–129, 2015, http://dx.doi.org/10.1007/978-3-319-19369-4_12.
  10. R. Panda, S. Agrawal, and S. Bhuyan, “Edge magnitude based multilevel thresholding using cuckoo search technique,” Expert Systems with Applications, vol. 40, no. 18, pp. 7617–7628, 2013.
  11. A. Mishra, C. Agarwal, A. Sharma, and P. Bedi, “Optimized grayscale image watermarking using dwt svd and firefly algorithm,” Expert Systems with Applications, vol. 41, no. 17, pp. 7858–7867, 2014.
  12. M. Woźniak and D. Połap, “Basic concept of cuckoo search algorithm for 2D images processing with some research results : An idea to apply cuckoo search algorithm in 2d images key-points search,” in SIGMAP 2014 - Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications, Part of ICETE 2014 - 11th International Joint Conference on e-Business and Telecommunications. 28-30 August, Vienna, Austria: SciTePress, 2014, pp. 157–164, http://dx.doi.org/10.5220/0005015801570164.
  13. M. Woźniak and Z. Marszałek, “An idea to apply firefly algorithm in 2D images key-points search,” Communications in Computer and Information Science - ICIST’2014, vol. 465, pp. 312–323, 2014, http://dx.doi.org/10.1007/978-3-319-11958-8_25.
  14. K. Waledzik and J. Mandziuk, “An automatically generated evaluation function in general game playing,” IEEE Trans. Comput. Intellig. and AI in Games, vol. 6, no. 3, pp. 258–270, 2014, http://dx.doi.org/10.1109/TCI-AIG.2013.2286825.
  15. M. Swiechowski and J. Mandziuk, “Self-adaptation of playing strategies in general game playing,” IEEE Trans. Comput. Intellig. and AI in Games, vol. 6, no. 4, pp. 367–381, 2014, http://dx.doi.org/10.1109/TCI-AIG.2013.2275163.
  16. L. Rutkowski, M. Jaworski, L. Pietruczuk, and P. Duda, “A new method for data stream mining based on the misclassification error,” IEEE Trans. Neural Netw. Learning Syst., vol. 26, no. 5, pp. 1048–1059, 2015, http://dx.doi.org/10.1109/TNNLS.2014.2333557.
  17. R. Grycuk, M. Gabryel, R. Scherer, and S. Voloshynovskiy, “Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database,” 2015, pp. 715–726, http://dx.doi.org/10.1007/978-3-319-19324-3_64.
  18. M. Korytkowski, L. Rutkowski, and R. Scherer, “Fast image classification by boosting fuzzy classifiers,” Information Sciences, vol. 327, pp. 175–182, 2016, http://dx.doi.org/10.1016/j.ins.2015.08.030.