Reversible image watermarking based on texture analysis of grey level co-occurrence matrix

Li, Shu zhi; Hu, Qin; Deng, Xiao Hong; Cai, Zhao

doi:10.1504/ijcse.2019.10020959

Cited by 1 publication

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“…The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems Keywords: optoelectronic image, camouflaged military equipment, genetic algorithm, chromosome population UDC 004.932 DOI: 10.15587/1729-4061.2022.259759 bright spots in the vicinity of this pixel. The advantage of the method [6] is the use of a small amount of memory. The disadvantages [6] are:…”

Section: Highlighting Brightness Channels In the Red-green-blue Color...mentioning

confidence: 99%

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Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

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The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems. The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – the use of a genetic algorithm in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space. Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems. A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way: – compared to the known k-means method, by an average of 15 % of errors of the first kind and an average of 7 % of errors of the second kind; – compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8 % of errors of the first kind and an average of 2.9 % of errors of the second kind. The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems

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Section: Highlighting Brightness Channels In the Red-green-blue Color...mentioning

confidence: 99%

“…In [6], the method of segmentation is proposed, based on the calculation of the matrix of matches. The essence of this method is to analyze for each pixel the image of signs formed on the basis of a matrix of coincidences, in which statistical information is collected about the mutual arrangement of proposed.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%