Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 49–56 (2017)
Abstract. The paper is devoted to the problem of automatic geometry evaluation of the log moving through the conveyor. The video sequence obtained from the single camera is used as the input data. The principal restrictions of the target objects described for the given task, and the requirements to the video recording of the manufacturing process are formulated on the basis of datasets from more than .5M video images. The authors' method for the video sequence segmentation in respect to the log tracking is presented. The algorithm is based on the combination of background subtraction techniques and probabilistic methods. Next part of the paper is devoted to the log geometry estimation methods. The authors' algorithm for the log geometry structure recovery is based on the detection, isolation and approximation of log boundaries. The results of the research are implemented in the development of the conveyor-tracking system for automatic log sorting.
- Janak K. (2007) Differences in roundwood measurements using. electronic 2D and 3D systems and Standard manual method. Drvna industrija Vol 58 (3) pp.127-133
- Zhang D, Lu G (2001) Segmentation of moving objects in image sequence: a review. Circuits Syst Signal Process 20(2):143–183
- Lipton A.J., Fujiyoshi H. Patil R.S. Moving target classification and tracking from real-time video. Applications of Computer Vision, 1998. WACV '98. Proceedings., Fourth IEEE Workshop on, Princeton, NJ, 1998, pp. 8-14. http://dx.doi.org/10.1109/ACV.1998.732851
- Cutler R., Davis L. View-based detection and analysis of periodic motion Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), Brisbane, Qld., 1998, pp. 495-500 vol.1. http://dx.doi.org/10.1109/ICPR.1998.711189
- Fablet R., Bouthemy P. Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics. IEEE-PAMI25(12), 1619–1624 (2003) http://dx.doi.org/10.1109/TPAMI.2003.1251155
- Hu M.K. Visual Pattern Recognition by Moment Invariants. IRE Trans. Info. Theory, vol. IT-8, pp.179–187, 1962
- Mahalanobis P.C. On the generalized distance in statistics. Proceedings of the National Institute of Sciences (Calcutta). – 1936, 2, 49–55.
- Cleveland W.S. Robust locally weighted regression and smoothing scatter plots. Journal of the American Statistical Association. – Vol. 74, no. 368. Pp. 829– 836. 1979
- Fischler M. A., Bolles R. C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6 (June 1981), 381-395. http://dx.doi.org/10.1145/358669.358692
- Lucas B.D., Kanade T. (1981) An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2 (IJCAI'81), Vol. 2. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 674-679. Stockman G., Shapiro L.G. (2001) Computer Vision (1st ed.). Prentice Hall PTR, Upper Saddle River, NJ, USA.
- Zivkovic Z. Improved adaptive Gausian mixture model for background subtraction International Conference Pattern Recognition, UK, August, 2004 http://dx.doi.org/10.1109/ICPR.2004.1333992
- Zivkovic Z., van der Heijden F. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006. http://dx.doi.org/10.1016/j.patrec.2005.11.005
- Prati A., Mikic C., Trivedi M. M., Cucchiara R. (2003) Detecting Moving Shadows: Algorithms and Evaluation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 25. no. 7. pp. 918‐ 923
- Koenderink J. J. What does the occluding contour tell us about solid shape? Perception 13 1984, 321 – 330.
- Shi J., Tomasi C. (1993) Good Features to Track. Technical Report. Cornell University, Ithaca, NY, USA.
- Harris, Stephens M. A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference: pages 147—151. 1988.
- Hartley R., Zisserman A. Multiple View Geometry in Computer Vision (2 ed.). Cambridge University Press, New York, NY, USA. 2003.
- Forsyth A.D., Ponce J. 2002. Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference.
- Kruglov A.V., Kruglov V.N. Tracking of fast moving objectsinrealtime. PatternRecognitionandImageAnalysis, 26(3):582–586, 2016 http://dx.doi.org/10.1134/S1054661816030111
- Wu K., Otoo E., Suzuki K. Optimizing two-pass connected- component labeling algorithms Pattern Anal Applic (2009) 12: 117. http://dx.doi.org/10.1007/s10044-008-0109-y
- Mulmuley K., Vazirani U.V., Vazirani V.V. Matching is as easy as matrix inversion Combinatorica (1987) 7: 105. http://dx.doi.org/10.1007/BF02579206
- Chiryshev Yu.V., Kruglov A.V. Detection of the moving objects in the problem of roundwood parameters estimation. Modern problems of science and education. – 2013. – No 11 (part 5) – P. 915-918
- Draper N. R., Smith H. Applied Regression Analysis, 3rd edn. New York, NY: John Wiley & Sons1998.
- Zhang R., Tsai P.-S., Cryer J. E. ShahM. Shape from Shading: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (8) (1999) 690–706 Cheung K., Baker S., Kanade T. Shape-From-Silhouette Across Time Part I:Theory and Algorithms Int J Comput Vision (2005) 62: 221. http://dx.doi.org/10.1007/s11263-005-4881-5