Synthesis of stochastic algorithms for image registration by the criterion of maximum mutual information

Synthesis of stochastic algorithms for image registration by the criterion of maximum mutual information

Автор: Tashlinskii A.G., Safina G.L., Ibragimov R.M.

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 1 т.48, 2024 года.

Бесплатный доступ

We discuss a synthesis of stochastic algorithms, obtaining expressions for gradients of Shannon, Renyi and Tsallis mutual information on the basis of the mathematical apparatus of stochastic gradient adaptation of algorithms for estimating image registration parameters. To obtain the expressions, derivatives of the image entropy with respect to the estimated parameters are used. The entropies are calculated using a Parzen window method. A comparative study of the synthesized algorithms in terms of stability and accuracy of the registration parameter estimates, including in conditions of additive noise, is carried out.

Image, estimation, parameter, binding, stochastic procedure, mutual information

Короткий адрес: https://sciup.org/140303265

IDR: 140303265   |   DOI: 10.18287/2412-6179-CO-1332

Список литературы Synthesis of stochastic algorithms for image registration by the criterion of maximum mutual information

  • Azam MA, Khan KB, Ahmad M, Mazzara M. Multimodal medical image registration and fusion for quality enhancement. Computers, Materials & Continua 2021; 68(1): 821-840. DOI: 10.32604/cmc.2021.016131.
  • Yu G, Zhao S. A new feature descriptor for multimodal image registration using phase congruency. Sensors 2020; 20(18): 5105. DOI: 10.3390/s20185105.
  • Gonzalez RC, Woods E. Digital image processing. London: Pearson; 2018.
  • Maes F, Vandermeulen D, Suetens P. Medical image registration using mutual information. Proc IEEE 2003; 91(10): 1699-1722. DOI: 10.1109/JPROC.2003.817864.
  • Fida AD, Gaidel AV, Demin NS, Ilyasova NY, Zamytskiy EA. Automated combination of optical coherence tomography images and fundus images. Computer Optics 2021; 45(5): 721-727. DOI: 10.18287/2412-6179-CO-892.
  • Dementiev VE, Magdeev RG, Tashlinskii AG. Detecting anomalies in temporal image sequences based on object identification by the stochastic gradient adaptation. 2021 Int Conf on Information Technology and Nanotechnology (ITNT) 2021: 1-5. DOI: 10.1109/ITNT52450.2021.9649175.
  • Kamaev AN, Karmanov DA. Visual navigation of an autonomous underwater vehicle based on the global search of image correspondences. Computer Optics 2018; 42(3): 457-467. DOI: 10.18287/2412-6179-2018-42-3-457-467.
  • Frolov VN, Tupikov VA, Pavlova VA, Alexandrov VA. Informational image fusion methods in multichannel optoelectronic systems [In Russian]. Izvetiya Tul’skogo Gosudarstvennogo Universiteta. Technicheskie Nauki 2016; 11(3): 95-104.
  • Zhang H, Xu R. Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta. Int J Appl Earth Obs Geoinf 2018; 64: 87-95. DOI: 10.1016/j.jag.2017.08.013.
  • Wan L, Xiang Y, You H. A post-classification comparison method for SAR and optical images change detection. IEEE Geosci Remote Sens Lett 2019; 16(7): 1026-1030. DOI: 10.1109/LGRS.2019.2892432.
  • Magdeev RG, Tashlinskii AG. A comparative analysis of the efficiency of the stochastic gradient approach to the identification of objects in binary images. Pattern Recogn Image Anal 2014; 24(4): 535-541. DOI: 10.1134/S1054661814040130.
  • Magdeev RG, Tashlinskii AG. Efficiency of object identification for binary images. Computer Optics 2019; 43(2): 277-281. DOI: 10.18287/2412-6179-2019-43-2-277-281.
  • Marcos D, Hamid R, Tuia D. Geospatial correspondences for multimodal registration. Proc IEEE Conf on Computer Vision and Pattern Recognition 2016: 5091-5100.
  • Park H, Bland PH, Brock KK, Meyer CR. Adaptive registration using local information measures. Med Image Anal 2004; 8(4): 465-473. DOI: 10.1016/j.media.2004.03.001.
  • Can A, Stewart C. A feature-based, robust, hierarchical algorithm for registration palm of images of the curved human retina. IEEE Trans Pattern Anal Mach Intell 2002; 24(3): 347-363. DOI: 10.1109/34.990136.
  • Maintz JBA, Viergever MA. A survey of medical image registration. Med Image Anal 1998; 2(1): 1-36. DOI: 10.1016/s1361-8415(01)80026-8.
  • Wu J, Cui Zh, Sheng VS, Zhao P, Su D, Gong Sh. A comparative study of SIFT and its variants. Meas Sci Rev 2013; 13(3): 122-131. DOI: 10.2478/msr-2013-0021.
  • Bay HV, Ess A, Tuytelaars T, Gool LV. SURF: Speeded up robust features. Comput Vis Image Underst 2008; 110(3): 346-359. DOI: 10.1016/j.cviu.2007.09.014.
  • Alexanin AI, Morozov MA, Fomin EV. The problems of image superimposition with one-pixel accuracy [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2019; 16(1): 9-16. DOI 10.21046/2070-7401-2019-16-1-9-16.
  • Zlobin VK, Kolesnikov AN, Kostrov BV. Correlationextreme methods of combining aerospace images [In Russian]. Vestnik of Ryazan State Radio Engineering University 2011; 37(3): 12-17.
  • Tashlinskij A.G, Tikhonov VO. Errors analysis technique for pseudogradient measurement of multidimensional processes parameters [In Russian]. Izvestiya Vysshikh Uchebnykh Zavedenij: Radioelektronika 2001; 44(9): 75-80.
  • Tashlinskii AG, Safina GL, Kovalenko RO, Ibragimov RM. Usage of mutual information as similarity measures for stochastic binding images. 2021 International Conference on Information Technology and Nanotechnology (ITNT) 2021: 1-6. DOI: 10.1109/ITNT52450.2021.9649386.
  • Tashlinskii A, Ibragimov R, Safina G. Application of Renyi mutual information in stochastic referencing of multispectral and multi-temporal images. 2022 VIII Int Conf on Information Technology and Nanotechnology (ITNT) 2022: 1-6. DOI: 10.1109/ITNT55410.2022.9848648.
  • Voronov SV, Tashlinskii AG. Efficiency analysis of information theoretic measures in image registration. Pattern Recogn Image Anal 2016; 26(3): 502-505. DOI: 10.1134/S1054661816030226.
  • Tsypkin YaZ. Information theory of identification [In Russian]. Moscow: "Fizmatlit" Publisher; 1995.
  • Tashlinskii AG, Safina GL, Voronov SV. Pseudogradient optimization of objective function in estimation of geometric interframe image deformations. Pattern Recogn Image Anal 2012; 22(2): 386-392. DOI: 10.1134/S1054661812020174.
  • Kovalenko RO, Tashlinskii AG. Optimization of the histogram intervals number which approximate brightness probability distributions in stochastic image alignment based on in formation similarity measures. 2022 24th Int Conf on Digital Signal Processing and its Applications (DSPA) 2022: 1-5. DOI: 10.1109/DSPA53304.2022.9805456.
  • Minkina GL, Samoilov MYu, Tashlinskii AG. Choice of the objective function for pseudogradient measurement of image parameters. Pattern Recogn Image Anal 2007; 17(1): 136-139.
  • Shannon CE, Weaver W. The mathematical theory of communication. Urbana: University of Illinois Press; 1998.
  • Cvejic N, Canagarajah CN, Bull DR. Image fusion metric based on mutual information and tsallis entropy. Electron Lett 2006; 42(11): 626-627. DOI: 10.1049/iel:20060693.
  • Renyi A. On measures of entropy and information. Proc Fourth Berkeley Symposium on Mathematical Statistics Probability 2006; 4.1: 547-561.
  • Mussa HY, Mitchell JBO, Afzalb AM. The Parzen Window method: In terms of two vectors and one matrix. Pattern Recogn Lett 2015; 63: 30-35. DOI: 10.1016/j.patrec.2015.06.002.
  • Viola P, Wells WM. Alignment by maximization of mutual information. Proc IEEE Int Conf on Computer Vision 1995: 16-23. DOI: 10.1109/ICCV.1995.466930.
  • Krasheninnikov VR. Fundamentals of the image processing theory [In Russian]. Ulyanovsk: ULSTU Publisher; 2003.
  • Tashlinskii, A.G. Optimal euclidean distance of estimates mismatch at stochastic gradient estimation of image interframe geometric deformation parameters [In Russian]. Informatsionno- Izmeritelnyye i Upravlyayushchiye Sistemy 2018; 11: 33-39.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp