TY - JOUR
KW - Finest Threshold
KW - Kapur’s Entropy
KW - Tsallis Entropy
KW - Moth-Flame-Optimization
KW - Image Processing
AU - V. Rajinikanth
AU - Seifedine Kadry
AU - Rubén González-Crespo
AU - Elena Verdú
AB - In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study.
IS - Regular Issue
M1 - 2
N2 - In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study.
PY - 2021
SP - 163
EP - 171
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm
UR - https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_15_0.pdf
VL - 7
SN - 1989-1660
ER -