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Identifying Coral Genera Using Median Binary Partition Texture Patterns

机译:使用中值二元分割纹理图案识别珊瑚属

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In this paper, underwater still images taken using the transect line method by marine scientists, were used to distinguish live coral textures from rubble/dead coral textures and sand textures. The live coral textures were further classified as either, Acropora, Montipora, Favia, Goniopora, or Porites, the top five coral genera found in the Philippines. A collection of 250 training texture images were used—50 for each coral genus, 131 samples for other coral types, 100 for sand, and 100 for rubble. Image processing (color normalization and median binary partition (MBP) for texture description) were conducted on the images. K-nearest neighbor (k-NN) classification was then used to classify the different texture regions. MBP proved useful in describing pixel relationships among texture regions, and when combined with k-NN, required less computational resources in classifying coral textures. Coral detection had a rate of 92.6 percent while sand and rubble/dead corals texture had detection rates of 93 percent and 84 percent, respectively. The effectiveness of MBP was reduced due to scale and multi-resolution limitations of the set of acquired images used for training and testing among the live corals classified, Porites had the highest detection rate (86%) followed by Acropora (58%), Montipora (54%), Favia (52%), and Goniopora (42%).
机译:在本文中,使用了海洋科学家使用断面线法拍摄的水下静止图像,将活珊瑚纹理与瓦砾/死珊瑚纹理和沙子纹理区分开。活珊瑚的纹理进一步分类为:Acropora,Montipora,Favia,Goniopora或Porites,这是菲律宾发现的前五名珊瑚属。使用了250个训练纹理图像的集合-每个珊瑚属50个,其他珊瑚类型131个样本,沙子100个,瓦砾100个。在图像上进行了图像处理(颜色归一化和中值二进制分区(MBP)以进行纹理描述)。然后使用K近邻(k-NN)分类对不同的纹理区域进行分类。 MBP被证明可用于描述纹理区域之间的像素关系,并且与k-NN结合使用时,在分类珊瑚纹理时需要较少的计算资源。珊瑚的检出率为92.6%,而沙子和瓦砾/死珊瑚的检出率分别为93%和84%。 MBP的有效性因分类的活珊瑚中用于训练和测试的采集图像集的规模和多分辨率限制而降低,Porites的检出率最高(86%),其次是Acropora(58%),Montipora (54%),Favia(52%)和Goniopora(42%)。

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