@article {5507,
	title = {Comparison of Manual and Semi-Automatic Underwater Imagery Analysis Approaches for Hard Bottom Benthic Microfauna Monitoring at Offshore Renewable Energy Installations},
	volume = {756, No. 1},
	year = {2015},
	month = {September},
	pages = {139{\textendash}153},
	publisher = {Springer},
	abstract = {The construction of new offshore wind farms is one of the strategies to fulfill growing demands for {\textquotedblleft}green{\textquotedblright} renewable energy. Underwater imagery is an important tool in the environmental monitoring of offshore renewable energy installations, especially in rocky benthic environment where traditional techniques, such as benthic grabs, are not applicable. Benthic features cover quantitative estimation from underwater imagery is a not easy task, especially when large amount of visual data must be processed in a short time. Underwater video from the high energy Norwegian Sea coast was used for this study. Traditional manual point-based benthic cover estimations from selected frames was tested against a semi-automatic color-based computer-assisted approach which involved making mosaic images from underwater videos. The study demonstrates that results of manual and semi-automatic benthic cover estimations are similar, although the manual analysis has a much larger spread in the variability of the data with many outliers due to the limited amount of points used in the analysis, compared to the semi-automatic analysis, where much larger proportion of the imagery is used. Although the number of benthic features that could be extracted by computer using color are fewer than those that can be detected with the human eye, the described semi-automatic method is less biased and less costly in terms of qualified staff. Implementation of the semi-automatic method does not reqiure any programing skills and has the ability to quickly and simply process larger amount of underwater imagery which would of decisive advantage to the industry.},
	keywords = {automatic image analysis, benthic cover estimation, features color, Underwater video, video mosaics},
	doi = {10.1007/s10750-014-2072-5},
	url = {http://link.springer.com/article/10.1007/s10750-014-2072-5},
	author = {Alex {\v S}a{\v s}kov and Thomas G. Dahlgren and Yuri Rzhanov and Marie-Lise Schl{\"a}ppy}
}
