Performance Evaluation of  Digital Image Processing by Using Scilab

Rudi Heriansyah; Wahyu Mulyo Utomo

doi:10.30595/juita.v9i2.8434

Authors

Rudi Heriansyah Universiti Kuala Lumpur http://orcid.org/0000-0003-1450-4712
Wahyu Mulyo Utomo <p>Universiti Tun Hussein Onn Malaysia</p>

DOI:

https://doi.org/10.30595/juita.v9i2.8434

Keywords:

Scilab, image processing, video processing, computational free software

Abstract

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

Author Biographies

Rudi Heriansyah, Universiti Kuala Lumpur

Dr. Rudi Heriansyah is currently with Computer Engineering Section, Malaysian Institute of Information Technology, Universiti Kuala Lumpur (UniKL-MIIT), Kuala Lumpur, Malaysia. His research interests are in digital image processing, computer vision, pattern recognition, artificial intelligence/machine learning/deep learning, internet of things, data science and cybersecurity.

Wahyu Mulyo Utomo, <p>Universiti Tun Hussein Onn Malaysia</p>

Dr. Wahyu is currently an Associate Professor with UTHM, Batu Pahat, Johor, Malaysia.

References

[1] M. Affouf, Scilab by Example. CreateSpace Independent Publishing Platform, 2012.

[2] B. R. Hunt, R. L. Lipsman, J. M. Rosenberg, K. R. Coombes, J. E. Osborn, and G. J. Stuck, A Guide to MATLAB: For Beginners and Experienced Users. Cambridge University Press, 2006.

[3] R. Fabbri, O. M. Bruno, and L. da F. Costa, “Scilab and SIP for Image Processing,” Mar. 2012.

[4] J. Q. Odeh, F. Ahmad, M. Othman, and R. Johari, “Image Retrieval System Based on Density Slicing of Colour Histogram of Images Subareas and Colour Pair Segmentation,” Int. Arab J. Inf. Technol., vol. 1, no. 2, pp. 196–202, 2004.

[5] J. Druel, “A SIP User Manual for SIP version 0.3 (rev. 1),” 2004.

[6] S. Yu and S. Shang, “SIVP – Scilab Image and Video Processing Toolbox,” 2006.

[7] H. Galda, “Image Processing with Scilab and Image Processing Design Toolbox,” 2011.

[8] J. S. Sohal, “Improvement of artificial neural network based character recognition system, using SciLab,” Optik (Stuttg)., vol. 127, no. 22, pp. 10510–10518, 2016.

[9] R. Senthilkumar and R. K. Gnanamurhty, “Improvement and solution to the problems arise in the implementation of facial image recognition algorithms using open source software scilab,” World Appl. Sci. J., vol. 34, no. 12, pp. 1754–1761, 2016.

[10] S. Chopparapu and B. Seventline, “Object detection using Matlab, Scilab and Python,” Technology, vol. 11, no. 6, pp. 101–108, 2020.

[11] A. Kaehler and G. Bradski, Learning OpenCV 3. O’Reilly Media, Inc., 2016.

[12] J. M. Kinser, Image Operators: Image Processing in Python. CRC Press, 2019.

[13] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. Pearson, 2018.

[14] C. Solomon and T. Breckon, Fundamentals of Digital Image Processing: A Practical Approach with Examples in MATLAB. Wiley-Blackwell, 2012.

[15] S. L. Tanimoto, An Interdisciplinary Introduction to Image Processing: Pixels, Numbers, and Programs. Massachusetts Institute of Technology, 2012.

[16] P. Selvakumar and S. Hariganesh, “The performance analysis of edge detection algorithms for image processing,” in 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), 2016, pp. 1–5.

[17] Z. Xu, X. Baojie, and W. Guoxin, “Canny edge detection based on Open CV,” in 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2017, pp. 53–56.

[18] A. McAndrew, A Computational Introduction to Digital Image Processing, 2nd ed. Taylor & Francis Group, LLC, 2016.

[19] N. Li, X. Lv, B. Li, and S. Xu, “An Improved Otsu Method Based on Uniformity Measurement for Segmentation of Water Surface Images,” in 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019, pp. 675–681.

[20] J. S. Hansen, GNU Octave Beginner’s Guide. Packt Publishing, 2011.

[21] G. Schafer and T. Cyders, “The Freemat 4.0 Primer,” 2011.