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Measuring torsional eye movements by tracking stable iris features.

Ong JK, Haslwanter T - J. Neurosci. Methods (2010)

Bottom Line: We propose a new method to measure torsional eye movements from videos taken of the eye.In this method, we track iris features that have been identified as Maximally Stable Volumes.These features, which are stable over time, are dark regions with bright borders that are steep in intensity.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Device Engineering, FH OO Forschungs & Entwicklungs GmbH, Upper Austria University of Applied Sciences, Garnisonstr 21, 4020 Linz, Austria. james.ong@fh-linz.at

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(a) Image derived by applying the polar transform to the raw image shown in Fig. 1. (b) The features that we use are shown superimposed on the polar transformed image in black. These features are found by using the Maximally Stable Volumes detector. We exclude features that are very dark, large, long in angular extent, or near the boundary.
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fig0010: (a) Image derived by applying the polar transform to the raw image shown in Fig. 1. (b) The features that we use are shown superimposed on the polar transformed image in black. These features are found by using the Maximally Stable Volumes detector. We exclude features that are very dark, large, long in angular extent, or near the boundary.

Mentions: In our application of the Maximally Stable Volumes detector, we choose the third dimension to be time, not space, which means that we can identify two-dimensional features that persist in time. The resulting features are maximally stable in space (2-D) and time (1-D), which means that they are 3-D intensity troughs with steep edges. Our implementation is based on the VLFeat library written by Vedaldi and Fulkerson (2008). However, the method of Maximally Stable Volumes is rather memory intensive, meaning that it can only be used for a small number of frames (in our case, 130 frames) at a time. Thus, we divide up the original movie into shorter overlapping movie segments for the purpose of finding features. We use an overlap of four frames, since the features become unreliable at the ends of each submovie. We set the parameters of the Maximally Stable Volumes detector such that we find almost all possible features. Of these features, we only use those that are near to the detected pupil centre (up to 6 mm away) and small (smaller than roughly 1% of the iris region). We remove features that are large in angular extent (the pupil and the edges of the eyelids), as well as features that are further from the pupil than the edges of the eyelids (eyelashes). We also remove features on the borders of the polar transformed images because these change size as they shift across the border, thus causing them to provide incorrect estimates of the torsional status of the eye. Fig. 2b shows the remaining features found in the image in Fig. 2a.


Measuring torsional eye movements by tracking stable iris features.

Ong JK, Haslwanter T - J. Neurosci. Methods (2010)

(a) Image derived by applying the polar transform to the raw image shown in Fig. 1. (b) The features that we use are shown superimposed on the polar transformed image in black. These features are found by using the Maximally Stable Volumes detector. We exclude features that are very dark, large, long in angular extent, or near the boundary.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2958308&req=5

fig0010: (a) Image derived by applying the polar transform to the raw image shown in Fig. 1. (b) The features that we use are shown superimposed on the polar transformed image in black. These features are found by using the Maximally Stable Volumes detector. We exclude features that are very dark, large, long in angular extent, or near the boundary.
Mentions: In our application of the Maximally Stable Volumes detector, we choose the third dimension to be time, not space, which means that we can identify two-dimensional features that persist in time. The resulting features are maximally stable in space (2-D) and time (1-D), which means that they are 3-D intensity troughs with steep edges. Our implementation is based on the VLFeat library written by Vedaldi and Fulkerson (2008). However, the method of Maximally Stable Volumes is rather memory intensive, meaning that it can only be used for a small number of frames (in our case, 130 frames) at a time. Thus, we divide up the original movie into shorter overlapping movie segments for the purpose of finding features. We use an overlap of four frames, since the features become unreliable at the ends of each submovie. We set the parameters of the Maximally Stable Volumes detector such that we find almost all possible features. Of these features, we only use those that are near to the detected pupil centre (up to 6 mm away) and small (smaller than roughly 1% of the iris region). We remove features that are large in angular extent (the pupil and the edges of the eyelids), as well as features that are further from the pupil than the edges of the eyelids (eyelashes). We also remove features on the borders of the polar transformed images because these change size as they shift across the border, thus causing them to provide incorrect estimates of the torsional status of the eye. Fig. 2b shows the remaining features found in the image in Fig. 2a.

Bottom Line: We propose a new method to measure torsional eye movements from videos taken of the eye.In this method, we track iris features that have been identified as Maximally Stable Volumes.These features, which are stable over time, are dark regions with bright borders that are steep in intensity.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Device Engineering, FH OO Forschungs & Entwicklungs GmbH, Upper Austria University of Applied Sciences, Garnisonstr 21, 4020 Linz, Austria. james.ong@fh-linz.at

Show MeSH
Related in: MedlinePlus