Abstract
The aim is to detect sudden temporal changes in image sequences, focusing on bright objects that appear in a few consecutive frames. The proposed algorithm detects such outliers by computing a variance weighted deviation from mean values for every pixel. On this result, an object segmentation based on 2D-moments and its invariants is done frame by frame at a ≈ 3σ threshold. The algorithm was designed for a wide range of tasks in pre-processing as a tool for detection of fast temporal changes such as suddenly appearing or moving objects. Two different applications on noisy sequence data were realized. The entire system proved to fulfill the requirements of industrial environments for online process control and scientific demands for data rejection.
The author thanks M.D. Schmidt and S. Hader for their comments and input.
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References
Aach, T., Kaup, A., Mester, R.: Bayesian Algorithms for Change Detection in Image Sequ. Using Markov Random Fields, Sig. Proc.-IC, vol. 7, no. 2, pp. 147–160, 1995
Beckman, R.J., Cook, R.D.: Outlier....s, Technometrics, vol. 25, pp. 119–149, 1983
Bouthemy, P., Lalande, P.: Detection and tracking of moving objects based on a statistical regularization method in space and time, Proc. ECCV, pp. 307–311, 1990
Brocke M., Schmidt, M. D. et al.: Verfahren zur autom. Beurteilung von Laserverarbeitungsprozessen, German Patent, DE 10103255, disclosure: 25.7.2002
Garbe, C., Jähne, B.: Reliable estimates of the sea surface heatflux from image sequences, In: B. Radig, Mustererkennung 2001, 23. DAGM-Symp., München
Hötter, M., Mester, R., Meyer, M.: Detection of moving objects in natural scenes, Proc. Carnahan Conf. on Security Technology, pp. 47–52, 1995
Hsu, Y. Z., Nagel, H. H. and Rekers, G.: New likelihood test methods for change detection in image sequences, CVGIP, vol. 26, pp. 73–106, 1984
Kreßel U. et al.: Polynomklassifikator vs. Multilayer-Perzeptron, In: R.E. Großkopf: Mustererkennung 1990, 12. DAGM-Symp. Oberkochen
Madalla, G. S., Yin Y.: Outliers, unit roots and robust estimation, In: G. S. Madalla et al: Handbook of Statistics, vol. 15, Elsevier, 1997
Nordbruch, S. et al.: Analyse von HDRC-Bildern des Werkstoffübergangs des MSG-Schweißprozesses, In: G. Sommer, Mustererkennung 2000, 22. DAGM-Symp., Kiel
Sethi, I., Patel, N.: A Statistical Approach to Scene Change Detection, SPIE Conf. Stor. and Retr. for Image and Video Database III, no. 2420, pp. 329–338
Teague, M. R.: Image Analysis via the General Theory of Moments, Opt. Soc. of America, vol. 70, no. 8, pp. 920–930, 1980
Thompson, W. R.: On a criterion for the rejection of observations, Ann. of Math. Stat., vol. 6, pp. 214–219, 1935
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Brocke, M. (2002). Statistical Image Sequence Processing for Temporal Change Detection. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_27
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DOI: https://doi.org/10.1007/3-540-45783-6_27
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