Measuring and evaluating the differences of compared images for a correct car silhouette categorization using integral transforms

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Abstract

The present paper focuses on the analysis of the possibilities of using integral transforms for measuring and evaluating the differences of compared images (car silhouettes) with the purpose of a correct car body categorization. Approaches such as the light intensities frequency change, the application of discrete integral transforms without the use of further supplementary information enabling automated data processing using the Fourier-Mellin transforms are used within this work. The calculation of the several metrics was verified through different combinations that implied using and not using the Hamming window and a low-pass filter. The paper introduced a method for measuring and evaluating the differences in the compared images (car silhouettes). The proposed method relies on the fact that the integral transforms have their own transformants in the case of translation, scaling and rotation, in the frequency area. Besides, the Fourier-Mellin transform was to offer image transformation that is resistant to the translation, rotation and scale.

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  • [1] Stylidis K. Wickman C. Söderberg R. (2015). Defining perceived quality in the automotive industry: An engineering approach. Procedia CIRP 36 (2015) 165-170.

  • [2] Bogue R. (2013). Robotic vision boosts automotive industry quality and productivity. Industrial Robot: An International Journal 40 (5) 415-419.

  • [3] Di Leo G. Liguori C. Pietrosanto A. Sommella P. (2017). A vision system for the online quality monitoring of industrial manufacturing. Optics and Lasers in Engineering 89 162-168.

  • [4] Ružarovský R. Delgado Sobrino D.R. Holubek R. Košťál P. (2014). Automated in-process inspection method in the flexible production system iCIM 3000. Applied Mechanics and Materials 693 50-55.

  • [5] Božek P. Pivarčiová E. (2013). Flexible manufacturing system with automatic control of product quality. Strojarstvo 55 (3) 211-221.

  • [6] Mery D. Jaeger T. Filbert D. (2002). A review of methods for automated recognition of casting defects. http://www.academia.edu/20111824/A_review_of_methods_for_automated_recognition_of_casting_defects.

  • [7] Świłło S.J. Perzyk M. (2013). Surface casting defects inspection using vision system and neural network techniques. Archives of Foundry Engineering 13 (4).

  • [8] Dhillon B.S. (2009). Human Reliability Error and Human Factors in Engineering Maintenance. CRC Press.

  • [9] Huang S.-H. Pan Y-Ch. (2015). Automated visual inspection in the semiconductor industry: A survey. Computers in Industry 66 1-10.

  • [10] Frankovský P. Ostertag O. Trebuňa F. Ostertagová E. Kelemen M. (2016). Methodology of contact stress analysis of gearwheel by means of experimental photoelasticity. Applied Optics 55 (18) 4856-4864.

  • [11] Kováč J. Ďurovský F. Hajduk M. (2014). Utilization of virtual reality connected with robotized system. Applied Mechanics and Materials 613 273-278.

  • [12] Frankovský P. Hroncová D. Delyová I. Hudák P. (2012). Inverse and forward dynamic analysis of two link manipulator. Procedia Engineering 48 158-163.

  • [13] Abramov I.V. Nikitin Yu.R. Abramov A.I. Sosnovich E.V. Božek P. (2014). Control and diagnostic model of brushless DC motor. Journal of Electrical Engineering 65 (5) 277- 282.

  • [14] Jena D.B. Kuma R. (2011). Implementation of wavelet denoising and image morphology on welding image for estimating HAZ and welding defect. Measurement Science Review 11 (4).

  • [15] Neogi N. Mohanta K.D. Dutta K.P. (2014). Review of vision-based steel surface inspection systems. EURASIP Journal on Image and Video Processing 2014 (50).

  • [16] Ito K. Nakajima H. Kobayashi K. Aoki T. Higuchi T. (2004). A fingerprint matching algorithm using Phase-Only Correlation. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E87-A (3) 682-691.

  • [17] Carl Zeiss Ltd. (2018). 3D inline measuring technology from ZEISS. https://www.zeiss.co.uk.

  • [18] Druckmüller M. Antoš M. Druckmüllerová H. (2005). Mathematical methods for visualization of the solar corona. Jemná mechanika a optika 10 302-304.

  • [19] van den Dool R. (2004). Fourier and Mellin Transform. Image Processing Tools. www.scribd.com/doc/9480198/Tools-Fourier-Mellin-Transform.

  • [20] Derrode S. Ghorbel F. (2001). Robust and efficient Fourier-Mellin transform approximations for graylevel image reconstruction and complete invariant description. Computer Vision and Image Understanding 83 (1) 57-78.

  • [21] Gueham M. Bouridane A. Crookes D. (2007). Automatic recognition of partial shoeprints based on phase-only correlation. In IEEE International Conference on Image Processing. IEEE Vol. 4 441-444.

  • [22] Chen Q.S. (1993). Image registration and its applications in medical imaging. Dissertation work Vrije University Brussels Belgium.

  • [23] Slížik J. Harťanský R. (2012). Metrology of electromagnetic intensity measurement in near field. Quality Innovation Prosperity 17 (1) 57-66.

  • [24] Hallon J. Kováč K. Bittera M. (2018). Comparison of coupling networks for EFT Pulses Injection. Przeglad elektrotechniczny 94 (2) 17-20.

  • [25] Harťanský R. Smieško V. Rafaj M. (2017). Modifying and accelerating the method of moments calculation. Computing and Informatics 36 (3) 664-682.

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