Backlight Compensation by Using a Novel Region of Interest Extraction Method


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 6, pp. 321-328, Jun. 2017
10.3745/KTSDE.2017.6.6.321,   PDF Download:
Keywords: region of interest, Backlight Compensation, Magnetic Lasso, Texture Extraction, k-Means Clustering
Abstract

We have implemented a technique to correct the brightness, saturation, and contrast of an image according to the degree of light, and further compensate the backlight. Backlight compensation can be done automatically or manually. For manual backlight compensation, we have to select the region of interest (ROI). ROI can be selected by connecting the outline of the desired object. We make users select the region delicately with the new magnetic lasso tool. The previous lasso tool has a disadvantage that the start point and the end point must be connected. However, the proposed lasso tool has the advantage of selecting the region of interest without connecting the start point and the end point. We can automatically obtain various results of backlight compensation by adjusting the number of k-means clusters for texture extraction and the threshold value for binarization.


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Cite this article
[IEEE Style]
J. M. Seong, S. S. Lee, S. Lee, "Backlight Compensation by Using a Novel Region of Interest Extraction Method," KIPS Transactions on Software and Data Engineering, vol. 6, no. 6, pp. 321-328, 2017. DOI: 10.3745/KTSDE.2017.6.6.321.

[ACM Style]
Joon Mo Seong, Seong Shin Lee, and Songwook Lee. 2017. Backlight Compensation by Using a Novel Region of Interest Extraction Method. KIPS Transactions on Software and Data Engineering, 6, 6, (2017), 321-328. DOI: 10.3745/KTSDE.2017.6.6.321.