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A Novel Model Driven Framework for Image Enhancement and Object Recognition

Part of the Communications in Computer and Information Science book series (CCIS,volume 1283)


Modern technological trends like Internet of Things (IoT’s) essentially require prompt development of software systems. To manage this, Model Driven Architecture (MDA) is frequently applied for development of different systems like industry automation, medical, surveillance, tracking and security etc. Image processing is an integral part of such systems. Particularly, image enhancement and classification operations are mandatory in order to effectively recognize objects for different purposes. Currently, such critical image processing operations are not managed through MDA and low level implementations are performed distinctly during system development. This severely delays the system development due to integration issues. Furthermore, system testing becomes problematic as few components of systems are developed through MDA and image processing operations are implemented in isolation. This article introduces a novel framework i.e. MIEORF – Model-driven Image Enhancement and Object Recognition Framework. Particularly, a meta-model is proposed, that allows modeling and visualization of complex image processing and object recognition tasks. Subsequently, an open source customized tree editor (developed using Eclipse Modeling Framework (EMF)) and graphical modeling tool/workbench (developed using Sirius) have been developed (both distributable via eclipse plugin). Consequently, the proposed framework allows modeling and graphical visualization of major image processing operations. Moreover, it provides strong grounds for model transformation operations e.g. Model to Text Transformations (M2T) using Acceleo for generating executable Matlab code. Furthermore, it systematically combines MDA and image processing concepts which are detailed enough to be easily integrated into wide variety of systems such as industrial automation, medical, surveillance, security and biometrics etc. The feasibility of proposed framework is demonstrated via real world medical imagery case study. The results prove that the proposed framework provides a complete solution for modeling and visualization of image processing tasks and highly effective for MDA based systems development.


  • Digital image processing
  • Model Driven Architecture
  • Image enhancement
  • Graphical modeling
  • Object recognition
  • Meta modeling
  • Sirius tool
  • Model based systems

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  • DOI: 10.1007/978-3-030-59506-7_2
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  1. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using MATLAB. Pearson Education India, Delhi (2004)

    Google Scholar 

  2. Solomon, C., Breckon, T.: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley, Hoboken (2011)

    Google Scholar 

  3. Burger, W., Burge, M.J.: Principles of Digital Image Processing: Fundamental Techniques. Springer, London (2010).

    CrossRef  MATH  Google Scholar 

  4. Goel, R., Jain, A.: The implementation of image enhancement techniques on color n gray scale IMAGEs. In: 2018 5th PDGC, Himachal Pradesh, India, pp. 204–209 (2018)

    Google Scholar 

  5. Singh, K.B., Mahendra, T.V., Rao, C.V.: Image enhancement with the application of local and global enhancement methods for dark images. In: IESC, pp. 199–202 (2017)

    Google Scholar 

  6. Sawant, H.K., Deore, M.: A comprehensive review of image enhancement techniques. IJCTEE 1(2), 39–44 (2010)

    Google Scholar 

  7. Maini, R., Aggarwal, H.: A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053 (2010)

  8. Cao, G., Zhao, Y., Ni, R., Tian, H.: Anti-forensics of contrast enhancement in digital images. In: 12th MM&amp, Security 2010, pp. 25–34. ACM, New York (2010)

    Google Scholar 

  9. Prasad, S., Abi-Nahed, J.: Contrast enhancement in wavelet domain for graph-based segmentation in medical imaging. In: ICVGIP 2012. ACM, New York (2012)

    Google Scholar 

  10. Cheng, N., Zhao, T., Chen, Z., Fu, X.: Enhancement of underwater images by super-resolution generative adversarial networks. In: 10th ICIMCS 2018, pp. 1–4. ACM, New York (2018). Article 22

    Google Scholar 

  11. Ucuzal, H., Balikçi Çiçek, A.G.İ., Arslan, A.G.A.K., Çolak, C.: A web-based application for identifying objects in images: object recognition software. In: 2019 3rd ISMSIT, Ankara, Turkey, pp. 1–5 (2019)

    Google Scholar 

  12. Tehsin, S., et al.: Improved maximum average correlation height filter with adaptive log base selection for object recognition. In: Optical Pattern Recognition XXVII (2016)

    Google Scholar 

  13. Panchal, P., et al.: A review on object detection and tracking methods. Int. J. Res. Emerg. Sci. Technol. 2(1), 7–12 (2015)

    MathSciNet  Google Scholar 

  14. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Int. 22(1), 4–37 (2000)

    CrossRef  Google Scholar 

  15. Anwar, M.W., Rashid, M., Azam, F., et al.: A model-driven framework for design and verification of embedded systems through SystemVerilog. Des. Autom. Embed. Syst. 23, 179–223 (2019).

    CrossRef  Google Scholar 

  16. Rasheed, Y., et al.: A model-driven approach for creating storyboards of web based user interfaces. In: 7th ICCCM. ACM (2019)

    Google Scholar 

  17. Davies, J., et al.: Model-driven engineering of information systems: 10 years and 1000 versions. Sci. Comput. Program. 89, 88–104 (2014)

    CrossRef  Google Scholar 

  18. Cuadrado, J.S., Izquierdo, J.L.C.: Applying model-driven engineering in small software enterprises. Sci. Comput. Program. 89, 176–198 (2014)

    CrossRef  Google Scholar 

  19. Martínez, S., Gérard, S., Cabot, J.: On watermarking for collaborative model-driven engineering. IEEE Access 6, 29715–29728 (2018)

    CrossRef  Google Scholar 

  20. Karasneh, B., Chaudron, M.R.V.: Img2UML: a system for extracting UML models from images. In: 2013 39th Euromicro Conference on Software Engineering and Advanced Applications, Santander, pp. 134–137 (2013)

    Google Scholar 

  21. Karasneh, B., Chaudron, M.R.V.: Extracting UML models from images. In: 2013 5th International Conference on Computer Science and Information Technology, Amman, pp. 169–178 (2013)

    Google Scholar 

  22. Ho-Quang, T., Chaudron, MR., Samúelsson, I., Osman, H.: Automatic classification of UML class diagrams from images. In: 2014 21st Asia-Pacific Software Engineering Conference, Jeju, pp. 399–406 (2014)

    Google Scholar 

  23. Ha, Y., Kim, B.: Shopping mall system with image retrieval based on UML. In: 2011 First ACIS International Symposium on Software and Network Engineering, Seoul, pp. 103–106 (2011)

    Google Scholar 

  24. Qasim, I., Anwar, M.W., Azam, F., Butt, W.H.: A model-driven mobile HMI framework (MMHF) for industrial control systems. J. IEEE Access 8, 10827–10846 (2020)

    CrossRef  Google Scholar 

  25. Anwar, M.W., Rashid, M., Azam, F., Naeem, A., Kashif, M., Butt, W.H.: A unified model-based framework for the simplified execution of static and dynamic assertion-based verification. IEEE Access 8, 104407–104431 (2020)

    CrossRef  Google Scholar 

  26. Rasheed, Y., Azam, F., Anwar, M.W.: A novel framework and tool for multi-purpose modeling of physical infrastructures. In: 12th ICCMS 2020, Brisbane, Australia (2020)

    Google Scholar 

  27. Gianni, D., Fuchs, J., De Simone, P., et al.: A modeling language to support the interoperability of global navigation satellite systems. GPS Solut. 17, 175–198 (2013).

    CrossRef  Google Scholar 

  28. Object management group, unified architecture framework (UAF). Accessed Feb 2020

  29. MIEORF Archives. Accessed Mar 2020

  30. Simulink image processing toolbox, Accessed Jun 2020

  31. Eclipse ImageN. Accessed Jun 2020

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Rasheed, Y., Abbas, M., Anwar, M.W., Butt, W.H., Fatima, U. (2020). A Novel Model Driven Framework for Image Enhancement and Object Recognition. In: Lopata, A., Butkienė, R., Gudonienė, D., Sukackė, V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham.

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