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2017 | 8 | 1 | 297-204

Article title

Web-Based API as a Tool in Teaching Computer Vision Concepts

Content

Title variants

Languages of publication

EN

Abstracts

EN
The article presents the stages of building Web-based API as a tool for teaching selected concepts of image processing server and shows a structure allowing for its implementation. During the implementation open source tools were used – graphical library OpenCV and Python programming language, which allows the implementation of both web part (by Django framework), as well as the cooperation with the above library. Idea of the creation of new Web-based API endpoints and the ability to change the parameters library OpenCV algorithms by modifying the URL in your browser were shown. Stages of creating each endpoint has been depicted with practical example on the image processing. Advantages and limitations of the proposed solution were presented.

Year

Volume

8

Issue

1

Pages

297-204

Physical description

Dates

published
2017

Contributors

  • Akademia Marynarki Wojennej w Gdyni, Wydział Nawigacji i Uzbrojenia Okrętowego, Zakład Informatyki, Polska

References

  • Benslimane, D., Dustdar, S., Sheth, A. (2008). Services Mashups: The New Generation of Web Applications. IEEE Internet Computing, 12, 13–15.
  • Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6), 679–698.
  • Cataldo, M., Mockus, A., Roberts, J., Herbsleb, J. (2009). Software Dependencies, Work Dependencies, and Their Impact on Failures. IEEE Transactions on Software Engineering, 35, 864–878.
  • Django (2017). Retrieved from: https://www.djangoproject.com/ (1.2017).
  • Lienhart, R., Maydt, J. (2002). An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP, 1, 900–903.
  • Makai, M. (2015). The Full Stack Python Guide to Deployments. San Francisco: Gumroad.
  • NumPy (2017). Retrieved from: http://www.numpy.org/ (1.2017).
  • OpenCV (2017). Retrieved from: http://docs.opencv.org/ (1.2017).
  • Requests, Retrieved from: http://docs.python-requests.org/en/latest/index.html (8.2015).
  • Richardson, L., Amundsen, M. (2013). RESTful Web APIs. Sebastopol: O’Reilly.
  • Viola, P., Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR.
  • Wikipedia about Mikhail Botvinnik. Retrieved from: https://en.wikipedia.org/wiki/Mikhail_ Botvinnik (8.2015).
  • Yi-Qing Wang (2014). An Analysis of the Viola-Jones Face Detection Algorithm. Image Processing On Line, 4, 128–148.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-9fe2b3db-7fb8-4bfe-98a7-b20281740417
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