Correlation properties of sea surface images on video stream frames

Document Type : Research Paper


1 Belgorod State University 85, Pobedy St., Belgorod, 308015, Russia

2 Universidad Autónoma del Carmen, Calle 56 No. 4 Esq Avenida Concordia Col, Benito Juàrez, Del Carmen, Campeche, 24180, Mexico


The work is devoted to the characteristic properties analysis of the sea surface image on video stream frames, which makes it possible to make a decision about the detection of object images on them in the absence of a priori information about objects, which is important in the development of navigation systems, security and surveillance systems. In this paper, it is proposes to analyze the values of the normalized cross-correlation coefficients of a given image fragment with higher dimension fragments on sequential video stream frames to identify the distinctive properties of the agitated sea surface and floating objects images. The computational experiments were carried out and have shown that the analysis of the results of calculating the frame fragments cross-correlations allows us to estimate the amount of displacement and distortion of a given image fragment. The results of computational experiments demonstrate the presence of differences in the values of the corresponding cross-correlation coefficients for the sea surface images with different agitation degrees, containing and not containing an image of the object. Based on the analysis of the proposed correlation properties of the sea surface images, a decisive rule for selecting fragments of frames containing an image of an object is formulated, the use of which, in many cases, allows to detection of fragments of object images correctly.


[1] H. Bay, A. Ess, L.V. Gool, and T. Tuytelaars, SURF: Speed up robust features, Comput. Vision Image Understand.  112 (2008), no. 3, 346–359.
[2] G. Carneiro and D. Lowe, Sparse flexible models of local features, Ninth Eur. Conf. Comput. Vision 3 (2006), 29–43.
[3] V. Golikov and O. Lebedeva, Adaptive detection of subpixel targets with hypothesis dependent background power, IEEE Signal Process. Aug. Lett. 21 (2013), no. 8, 751–754.
[4] V. Golikov, M. Rodriguez-Blanco, and O. Lebedeva, Robust multipixel matched subspace detection with signaldependent background power, J. Appl. Remote Sens. 10 (2016), no. 1, 015006–015006.
[5] V. Golikov, O. Samovarov, E. Zhilyakov, J.L. Rullan-Lara, and H. Alazki, Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix, J. Appl. Remote Sens. 14 (2020), no. 4, 046513–046513.
[6] R.C. Gonzales and P. Wintz, Digital Image Processing, Addison-Wesley Longman Publishing Co., Inc., 1987.
[7] A. Goshtasby, S.H. Gage, and J.F. Bartholic, A two-stage cross correlation approach to template matching, IEEE Trans. Pattern Anal. Machine Intell. 6 (1984), no. 3, 374–378.
[8] R.M. Haralick and L.G. Shapiro, Computer and Robot Vision, Addison-Wesley, 1992.
[9] B.K.P. Horn and B.G. Schunck, Determining optical flow, Artif. Intell. 17 (1981), 185–213.
[10] R. Kerekes and B.V. Kumar, Enhanced video-based target detection using multi-frame correlation filtering, IEEE Trans. Aerospace Electronic Sys. 45 (2009), no. 1, 289–307.
[11] L.L. Scharf and B. Friedlander, Matched subspace detectors, IEEE Trans. Signal Process. 42 (1994), no. 8, 2146–2157.
[12] C. Stauffer and W.E.L. Grimson, Adaptive background mixture models for real-time tracking, Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recogn. (Cat. No PR00149), IEEE 2 (1999), 246–252.
[13] J. Theiler, Quantitative comparison of quadratic covariance-based anomalous change detectors, Appl. Opt. 47 (2008), no. 28, F12–F26.
[14] Y.C. Tzeng, D.M. Chu, M.F. Wu, and C. Kun-Shan, Automatic detection of targets using fractal dimension, IEEE Geosci. Remote Sens. Symp. 3 (2005), 1713–1716.
[15] Z. Wang and J.-H. Xue, The matched subspace detector with interaction effects, Pattern Recognition, 68 (2017), 24–37.
[16] E.G. Zhilyakov, A.A. Chernomorets, E.V. Bolgova, and A.N. Kovalenko, Image decomposition on the orthogonal basis of subband matrices eigenvectors, J. Eng. Appl. Sci. 12 (2017), 3194–3197.
[17] E.G. Zhilyakov, A.A. Chernomorets, E.V. Bolgova, D.V. Ursol, and E.I. Prokhorenko, On comparing the energy concentration of the image orthogonal transforms, J. Adva. Res. Dyn. Control Syst. 12 (2020), no. 6, 692–722.
[18] E.G. Zhilyakov, V. Golikov, D.A. Chernomorets, O.I. Samovarov, and S.L. Babarinov, Detection of Slow-moving objects floating on an agitated sea surface based on subband analysis within the cosine transform, J. Adv. Res. Dyn. Control Syst. 12 (2020), no. 25, 1314–1325.
Volume 14, Issue 12
December 2023
Pages 343-350
  • Receive Date: 08 June 2021
  • Revise Date: 20 October 2021
  • Accept Date: 26 October 2021