Computer Vision for Color Detection
Keywords:
Color Detection, Computer Vision, RGB Color Space, Shortest Distance AlgorithmAbstract
Humans see the world in colors. When it comes to the aspect of just looking, all it does is please the eyes but when it comes to questioning its make, it becomes a challenge. It is much easier to be served the values without the tedious task of finding a person who understands colors. This paper proposes the idea of teaching a computer to detect and define a color well enough to have useful applications. The detection algorithm proposed uses the advantage of the camera and fed in data to detect even the color based on RGB values. The algorithm involved calls on a function that runs loops on readjusting the distance based on a nearest match. This effortlessly helps define a color based on the RGB color space with a peaking accuracy.
Downloads
References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson (2018)
Project in Python – Color Detection using Pandas & OpenCV. https://data-flair.training/blogs/project-in-python color-detection/
Jake Frankenfield, Gordon Scott. Artificial Intelligence (AI). https://www.investopedia.com/terms/a/artificial-intelligence ai.asp#:~:text=Artificial%20intelligence%20(AI)%20refers% 20to,as%20learning%20and%20problem%2Dsolving.
Victor Wiley, Thomas Lucas. Computer Vision and Image Processing: A Paper Review. International Journal Of Artificial Intelegence Research. ISSN: 2579-7298, Vol 2, No 1, June 2018, pp. 28-36 DOI: 10.29099/ijair.v2i1.42
Computer Vision. What it is and why it matters. https://www.sas.com/en_in/insights/analytics/computer vision.html
R. S. Berns, “Principles of Color Technology” (3rd edition New York: Wiley, 2000)
Yang, F., Lu, H., Zhang, W., Yang, G.: ‘Visual tracking via bag of features’, IET Image Process., 2012, 6, pp. 115–128 (doi: 10.1049/iet-ipr.2010.0127)
Hasting, G. & Rubin, Alan. (2012). Color spaces - a review of historic and modern color models*. African Vision and Eye Health. 71. 10.4102/aveh.v71i3.76.
Behic Guven. Building a Color Recognizer in Python. Towards Data Science. https://towardsdatascience.com/building-a-color-recognizer in-python-4783dfc72456
G. Wyszecki and W. S. Styles, “Color Science: Concepts and Methods, Quantitative Data and Formulae” (2nd edition New York: Wiley, 1982)
Berns, RS & Reiman DM, “Color managing the third edition of Billmeyer and Saltzman's Principles of Color Technology”, Color Research & Application, Vol.27, No.5, (2002), pp.360-373.
Gonzalez RC, Woods RE & Eddins S.L, Digital Image Processing Using MATLAB, Pearson Education, Inc, (2004). [13] Arash Abadpour and Shohreh Kasaei. Principal Color and Its Application to Color Image Segmentation. http://abadpour.com/files/html/178si/W178si.html [14] Senthamaraikannan D, Shriram S & William J, “Real time color recognition”, International Journal of Innovative Research In Electrical,Electronics, Instrumentation And Control Engineering, Vol.2, No.3,(2014).
Karan Bhanot. Color Identification in Images. Towards Data Science. https://towardsdatascience.com/color-identification in-images-machine-learning-application-b26e770c4c71
Ray Siddheswar, and Rose H. Turi. "Determination of number of clusters in k-means clustering and application in color image segmentation." In Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, pp. 137-143. 1999.
Shun-Hung Tsai, Yu-Hsiang Tseng, A novel color detection method based on HSL color space for robotic soccer competition, Computers & Mathematics with Applications, Volume 64, Issue 5, 2012, Pages 1291-1300, ISSN 0898- 1221, https://doi.org/10.1016/j.camwa.2012.03.073.
John Kennedy, Steven White. CMY and CMYK Color Spaces. https://docs.microsoft.com/en us/windows/win32/wcs/cmy-and-cmyk-color-spaces
OpenCV Python Tutorial – Implementation of Computer Vision https://data-flair.training/blogs/opencv-python tutorial/
Resti, Yulia & Burlian, Firmansyah & Yani, Irsyadi & Rosiliani, Dinda. (2020). Analysis of a cans waste classification system based on the CMYK color model using different metric distances on the k-means method. Journal of Physics: Conference Series. 1500. 012010. 10.1088/1742- 6596/1500/1/012010.
Cheng-Jin Du, Da-Wen Sun, 18 - Quality Evaluation of Pizzas, Editor(s): Da-Wen Sun, In Food Science and Technology, Computer Vision Technology for Food Quality Evaluation, Academic Press, 2008, Pages 427-446, ISBN 9780123736420, https://doi.org/10.1016/B978-012373642- 0.50021-1.