White Patch on the Fore-Flipper of Common Minke Whale, as a Potential Morphological Index to Identify Stocks

Past studies have indicated that there are two different stocks (J and O stocks) in the North Pacific common minke whale Balaenoptera acutorostrata population. These two stocks differ from each other in certain characteristics, such as body size, conception dates, and genetics. However, till date, few studies have investigated differences in the external body appearance between the two stocks. Therefore, in the present study, we focused on the unique white patch on the flipper of this whale species to elucidate inter-stock differences. We used the animals collected from JARPNII research during 2012 and 2013;stock information was determined by microsatellite DNA analysis (n = 220). We focused on the morphological differences in the size and pattern of the white patch on the flipper of each whale. The length of the white patch along the anterior (ventral) margin of the flipper tends to be proportionally larger in O stock. The pattern of the boundary area of the white patch named as the “Grayish Accessary Layer (GAL)” was remarkably different between stocks. Within animals with “no GAL” type, 94% were J stock. Conversely, of animals with GAL expanding over the half the flipper width, 96% were O stock. We concluded from our study that there were clear morphological differences in the flipper color pattern between J and O stocks, which enable to apply widely to other individuals.

基于超像素暗通道和自动色阶优化的图像去雾算法

针对暗通道先验(dark channel prior,DCP)复原图像中的光晕现象、明亮区域色彩失真、环境光估计不准确等问题,提出了基于超像素暗通道和自动色阶优化的单幅图像去雾算法。首先,由改进的White Patch Retinex算法增强图像并计算精确环境光。接着,在传统暗通道去雾算法中引入超像素图像分割和引导滤波算法,使透射率估计的稳健性与精确性得以提升。然后,采用自适应容差对明亮区域的透射率进行补偿,有效抑制明亮区域色彩失真问题。最后,以自动色阶优化算法提高图像对比度。将本文去雾算法与其他算法从主观和客观两个维度进行比较,实验结果表明:采用不同算法对不同浓度的自然雾图进行对比实验,信息熵提高0.2 bit,峰值信噪比(peak signal-to-noise ratio,PSNR)提高0.8 dB,运行效率提高。该算法对不同浓度含雾图像具有良好的适应性,复原图像色彩真实、纹理清晰、细节丰富,去雾效果良好。