2020 Researchable Dissertation Topics In Digital Imaging

Brief

  • Digital imaging is one of the important dissertation topics in the year 2020.
  • Digital imaging is the creation of digital images, such as the physiological scene or an object’s internal structure
  • Digital imaging used in the medical field, Video processing, image sharpening and restoration, Remote sensing, Color processing, Transmission and encoding, Robot vision, Microscopic Imaging, and Pattern Recognition.

Digital Imaging

Digital imaging is the development of digital images, like the physiological scene or the internal framework of an artifact. Sometimes, the term implies or involves the storage, encoding, compression, display, the printing of these images. Digital images could be defined by the sort of electromagnetic radiation or other waves that variable amplification expresses the information which forms the picture when they travel through or bounce off artifacts. In all digital imaging groups, image sensors transform the information into digital signals which are interpreted by a machine and generated as a visible-light image.

For instance, the visible light-medium makes digital photography possible with different sorts of digital cameras. X-rays enable digital X-ray vision, and gamma rays require digital ray imaging. Sound facilitates ultrasonography and sonar, with radio waves allowing for radar. Digital photography allows for software-based image analysis and image editing[2]. Digital imaging is one of the important dissertation topics in the year 2020. Few significant dissertation topics are explained in detail below,

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1. Dynamic stay-cable strength measurement utilizing digital imaging techniques :

With rapid technological and economic growth, bridges were an integral aspect of modern transportation across the world. The condition of several bridge systems is not promising, owing to high traffic volumes and incredibly complex conditions. The development of the Structure Health Monitoring (SHM) for bridges is therefore vital. As a crucial load-bearing component, the cable plays an essential role in the overall security of the bridge structure, and thus the cable strength, measured as per its complex properties, requires to be concentrated on. The dynamic structures must be defined to determine the pressure of the cable that is continuously influenced by external factor through the usage of traffic. The cable force can currently be measured using the pressure sensor, vibration system, the pressure oil meter and the magnetic flux [1-4].

During bridge construction, the pressure oil meter systems as well as the pressure sensor, which belongs to the direct technique, are commonly applied to cable force measurement. Magnetic flux is good for non-contact calculation and long-term monitoring, though initial costs are high and the usability of the system requires to be enhanced. Vibration approach is widely utilized in bridge framework monitoring with the installation of acceleration sensors to capture the dynamic reaction. This implies that too many cabling services are needed to fulfil the data transmission requirements. Sensor management and maintenance are often confronted with a lot of difficulties. In order to resolve the above issues, a digital image technology join the monitoring and identification of materials in the engineering structure [5-7] and has been extensively established [8-9]. Some of the 2020 dissertation topic of digital imaging is given below.

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2. The measurement of surface coloration by digital image processing in meat products:

Iridescence is generally assessed by sensory examination but it is a cost-intensive and time-consuming and process. Digital image analysis is a cost-effective, efficient, and unbiased alternative. The growth of an image analysis technique for quantifying iridescence in meat products is reported here. Two methods of segmentation have been tested for their capacity to divide images into segments of non-iridescent and iridescent areas. Findings from the research have shown that digital image processing is a useful tool for the evaluation of surface iridescence in meat products.

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3. Digital image-depend tracing of geographical origin, winemaker and variety of grape for identification of the red wine:

This research illustrates the possibility of using colour histograms acquired from digital images for the identification of red wine samples in the São Francisco Valley region; by geographical origin, by grape variety, by winemaker and by using chemical modelling. The methodology established is quick, easy, and affordable, consuming very low sample volumes; requiring no pre-treatments, toxic solvents, chemical reagents, or and being consistent with Green Chemistry values. In addition, it might also serve as an effective analytical tool to track red wines manufactured in the São Francisco Valley area, offering an advantage 14 over potential approved geographic indicator labelling. Nonetheless, a wider and more diverse study of red wine specimens utilizing more varieties, harvest years, wineries, and regional sources should be applied in order to ensure the generalization of the suggested technique.

Some of the 2020 dissertation topic of digital imaging is given below.

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List of 2020 dissertation topic of digital imaging

1.Stretch field asphalt mortar distribution utilizing digital image processing

2.Tropospheric ozone assessment by digital image processing with a smartphone camera

3.Best digital image colour correlation

4.Measurement of the 2D anisotropic distortion through situ electron microscopy scanning as well as digital image interpretation

5.An environmentally friendly spot test technique for the micro-titration of citric fruit with digital imaging

6. Digital image correlation to compensate for systematic errors due to impaired form functions

7. Failure to classify CF/epoxy V-form elements by means of digital image comparison and acoustic emission evaluation

Conclusion

Digital imaging is one of the important dissertation topics in the year 2020. Digital imaging is the creation of digital images, such as the physiological scene or an object’s internal structure.

Digital imaging used in the medical field, Video processing, image sharpening and restoration, Remote sensing, Color processing, Transmission and encoding, Robot vision, Microscopic Imaging, and Pattern Recognition.

Referred Blog

References

    1. Albizu, G., Bordagaray, A., Dávila, S., Garcia-Arrona, R., Ostra, M., & Vidal, M. (2020). Analytical control of nickel coating baths by digital image analysis. Microchemical Journal, 154, 104600. https://doi.org/10.1016/j.microc.2020.104600
    2. Bai, P., Xu, Y., Zhu, F., & Lei, D. (2020). A novel method to compensate systematic errors due to undermatched shape functions in digital image correlation. Optics and Lasers in Engineering, 126, 105907. https://doi.org/10.1016/j.optlaseng.2019.105907
    3. Cerrato-Alvarez, M., Frutos-Puerto, S., Miró-Rodríguez, C., & Pinilla-Gil, E. (2020). Measurement of tropospheric ozone by digital image analysis of indigotrisulfonate-impregnated passive sampling pads using a smartphone camera. Microchemical Journal, 154, 104535. https://doi.org/10.1016/j.microc.2019.104535
    4. Chen, C.-C., Wu, W.-H., Leu, M.-R., & Lai, G. (2016). Tension determination of stay cable or external tendon with complicated constraints using multiple vibration measurements. Measurement, 86, 182–195. Retrieved from https://www.sciencedirect.com/science/article/pii/S0263224116001366
    5. Chen, Z., Li, H., Bao, Y., Li, N., & Jin, Y. (2016). Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technology. Structural Control and Health Monitoring, 23(3), 517–534. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/stc.1780
    6. Curt, J., Capaldo, M., Hild, F., & Roux, S. (2020). Optimal digital color image correlation. Optics and Lasers in Engineering, 127, 105896. https://doi.org/10.1016/j.optlaseng.2019.105896
    7. de Oliveira, L. M. A., dos Santos, V. B., da Silva, E. K. N., Lopes, A. S., & Dantas-Filho, H. A. (2020). An environment-friendly spot test method with digital imaging for the micro-titration of citric fruits. Talanta, 206, 120219. https://doi.org/10.1016/j.talanta.2019.120219
    8. Galer, M., & Horvat, L. (2005). Digital Imaging. Elsevier/Focal Press. Retrieved from https://books.google.co.in/books?id=vYIMZXSiHYAC
    9. Gao, Z., Li, F., Liu, Y., Cheng, T., Su, Y., Fang, Z., … Zhang, Q. (2020). Tunnel contour detection during construction based on digital image correlation. Optics and Lasers in Engineering, 126, 105879. https://doi.org/10.1016/j.optlaseng.2019.105879
    10. Garcés, G., Orozco-Caballero, A., Quinta da Fonseca, J., Pérez, P., Medina, J., Stark, A., … Adeva, P. (2020). Initial plasticity stages in Mg alloys containing Long-Period Stacking Ordered phases using High Resolution Digital Image Correlation (HRDIC) and in-situ synchrotron radiation. Materials Science and Engineering: A, 772, 138716. https://doi.org/10.1016/j.msea.2019.138716
    11. Ho, H.-N., Kim, K.-D., Park, Y.-S., & Lee, J.-J. (2013). An efficient image-based damage detection for cable surface in cable-stayed bridges. Ndt & E International, 58, 18–23. Retrieved from https://www.sciencedirect.com/science/article/pii/S0963869513000625
    12. Mahal, M., Blanksvärd, T., Täljsten, B., & Sas, G. (2015). Using digital image correlation to evaluate fatigue behavior of strengthened reinforced concrete beams. Engineering Structures, 105, 277–288. https://doi.org/10.1016/j.engstruct.2015.10.017
    13. Ruedt, C., Gibis, M., & Weiss, J. (2020). Quantification of surface iridescence in meat products by digital image analysis. Meat Science, 163, 108064. https://doi.org/10.1016/j.meatsci.2020.108064
    14. Shimada, T. (1995). A study on the maintenance and management of the tension measurement for the cable of bridge. Dissertation,(Kobe University, 1995). Retrieved from https://sci-hub.se/https://www.sciencedirect.com/science/article/pii/S0263224119310772
    15. Spencer Jr, B. F., Hoskere, V., & Narazaki, Y. (2019). Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering. Retrieved from https://www.sciencedirect.com/science/article/pii/S2095809918308130
    16. Szabo, T. L. (2004). Imaging Systems And Applications. In Diagnostic Ultrasound Imaging (pp. 297–336). Elsevier. https://doi.org/10.1016/B978-012680145-3/50011-6
    17. Szabo, T. L. (2014). Imaging Systems and Applications. In Diagnostic Ultrasound Imaging: Inside Out (pp. 365–430). Elsevier. https://doi.org/10.1016/B978-0-12-396487-8.00010-0
    18. Tao, R., Zhu, J., Zhang, Y., Song, W., Chen, H., & Fang, D. (2020). Quantifying the 2D anisotropic displacement and strain fields in graphite-based electrode via in situ scanning electron microscopy and digital image correlation. Extreme Mechanics Letters, 100635. https://doi.org/10.1016/j.eml.2020.100635
    19. Wu, W.-H., Chen, C.-C., Chen, Y.-C., Lai, G., & Huang, C.-M. (2018). Tension determination for suspenders of arch bridge based on multiple vibration measurements concentrated at one end. Measurement, 123, 254–269. Retrieved from https://www.sciencedirect.com/science/article/pii/S0263224118302586
    20. Xing, C., Xu, H., Tan, Y., Wang, D., & Zhai, C. (2020). Strain field distribution of asphalt mortar using digital image processing. Construction and Building Materials, 238, 117624. https://doi.org/10.1016/j.conbuildmat.2019.117624
    21. Zui, H., Shinke, T., & Namita, Y. (1996). Practical formulas for estimation of cable tension by vibration method. Journal of Structural Engineering, 122(6), 651–656. Retrieved from https://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9445(1996)122:6(651)

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