@article{oai:kyutech.repo.nii.ac.jp:00004697,
 author = {Tan, Yasuhiro and Tan, Joo kooi and タン, ジュークイ and Kim, Hyungseop and 金, 亨燮 and 石川, 聖二 and Ishikawa, Seiji},
 issue = {1},
 journal = {International Journal of Biomedical Soft Computing and Human Sciences},
 month = {May},
 note = {Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. We propose in this paper a method that applies a higher-order local auto-correlation feature and a subspace method to the acoustic image provided by the side-scan sonar to classify seabed sediment automatically. In texture classification, the proposed method outperformed other methods such as a gray level co-occurrence matrix and a Local Binary Pattern operator. Experimental results show that the proposed method produces consistent maps of a seafloor.},
 pages = {43--50},
 title = {Classifying seabed sediments using local auto-correlation features},
 volume = {19},
 year = {2013},
 yomi = {イシカワ, セイジ}
}