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      Automatic individual identification of Saimaa ringed seals

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          Abstract

          In order to monitor an animal population and to track individual animals in a non-invasive way, identification of individual animals based on certain distinctive characteristics is necessary. In this study, automatic image-based individual identification of the endangered Saimaa ringed seal ( Phoca hispida saimensis) is considered. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual. This can be used as a basis for the identification process. The authors propose a framework that starts with segmentation of the seal from the background and proceeds to various post-processing steps to make the pelage pattern more visible and the identification easier. Finally, two existing species independent individual identification methods are compared with a challenging data set of Saimaa ringed seal images. The results show that the segmentation and proposed post-processing steps increase the identification performance.

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          Most cited references 12

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          Superpixel classification based optic disc and optic cup segmentation for glaucoma screening.

           Tin Aung,  Damon Wong,  Y. Xu (2013)
          Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.
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            Global threats to pinnipeds

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              Automated identification of animal species in camera trap images

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                Author and article information

                Contributors
                Journal
                IET-CVI
                IET Computer Vision
                IET Comput. Vis.
                The Institution of Engineering and Technology
                1751-9632
                1751-9640
                24 October 2017
                18 December 2017
                March 2018
                : 12
                : 2
                : 146-152
                Affiliations
                [1 ] Machine Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta University of Technology , Lappeenranta, Finland
                [2 ] Department of Environmental and Biological Sciences, University of Eastern Finland , Joensuu, Finland
                [3 ] Parks & Wildlife Finland, State Forest Enterprise (Metsähallitus) , Savonlinna, Finland
                [4 ] Natural Resources Institute Finland , FI-80100 Joensuu, Finland
                Article
                IET-CVI.2017.0082 CVI.SI.2017.0082.R3
                10.1049/iet-cvi.2017.0082

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                Page count
                Pages: 0
                Product
                Categories
                Special Issue: Computer Vision for Animal Biometrics

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