基于计算机视觉的 3 种金枪鱼属鱼类表型纹理特征分析

Tuna is an important commercial product and accounts for a large proportion of the world's fisheries. At the same time, tuna also has a great impact on the development of China’s fishery production. The genus Thunnus is an important catch resource in China’s pelagic fishery, and its phenotype texture information is not only characteristic of fish species, but also can be used as a scientific basis for classification. Traditional fish texture feature analysis has mainly used qualitative description analysis (QDA), but computer vision technology can provide quantitative analysis (QA) data instead. This paper used computer vision to pre-locate the basic standard point of the images of three Thunnus species, determining the texture feature regions by moving the basic standard point and automatically acquiring them. The texture image was transformed into and quantized with gray level. The quantized gray level image was used for gray level co-occurrence matrix (GLCM) calculation, and the obtained GLCM was normalized. Six texture indexes were calculated by normalized gray co-occurrence matrix, and the variation trend of the distance and direction of texture indexes was analyzed. The texture indexes of the genus Thunnus were studied by factor analysis (FA). Through texture analysis of computer vision, the results show that the extraction effect of texture index for the three Thunnus species was good. When the distance value was 4, the change trend of texture index tended to be stable, the direction of texture index of the three Thunnus species changed, and its mean direction was representative. Factor analysis of the three Thunnus species shows that the contribution rate of the first principal component was 81.10%, indicating that the extracted six texture indexes had high significance and good effect.