Improved content aware scene retargeting for retinitis pigmentosa patients
Identifieur interne : 000604 ( Main/Exploration ); précédent : 000603; suivant : 000605Improved content aware scene retargeting for retinitis pigmentosa patients
Auteurs : Walid I. Al-Atabany [Royaume-Uni, Égypte] ; Tzyy Tong [Royaume-Uni] ; Patrick A. Degenaar [Royaume-Uni]Source :
- BioMedical Engineering OnLine [ 1475-925X ] ; 2010.
Abstract
In this paper we present a novel scene retargeting technique to reduce the visual scene while maintaining the size of the key features. The algorithm is scalable to implementation onto portable devices, and thus, has potential for augmented reality systems to provide visual support for those with tunnel vision. We therefore test the efficacy of our algorithm on shrinking the visual scene into the remaining field of view for those patients.
Simple spatial compression of visual scenes makes objects appear further away. We have therefore developed an algorithm which removes low importance information, maintaining the size of the significant features. Previous approaches in this field have included
We have evaluated and compared our algorithm to the
Our approach allows us to perform content aware video resizing in real time using only information from previous frames to avoid jitter. Also our method has a great benefit over the ordinary resizing method and even over other image retargeting methods. We show that the benefit derived from this algorithm is significant to patients with fields of view 20° or less.
Url:
DOI: 10.1186/1475-925X-9-52
PubMed: 20846440
PubMed Central: 2949883
Affiliations:
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Al-Atabany</name><affiliation wicri:level="3"><nlm:aff id="I1">Department of Bioengineering, Imperial College London, London, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Bioengineering, Imperial College London, London</wicri:regionArea><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="I4">Department of Biomedical Engineering, Helwan University, Egypt</nlm:aff><country xml:lang="fr">Égypte</country><wicri:regionArea>Department of Biomedical Engineering, Helwan University</wicri:regionArea><wicri:noRegion>Helwan University</wicri:noRegion></affiliation></author><author><name sortKey="Tong, Tzyy" sort="Tong, Tzyy" uniqKey="Tong T" first="Tzyy" last="Tong">Tzyy Tong</name><affiliation wicri:level="3"><nlm:aff id="I2">Department of Computing, Imperial College London, London, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Computing, Imperial College London, London</wicri:regionArea><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation></author><author><name sortKey="Degenaar, Patrick A" sort="Degenaar, Patrick A" uniqKey="Degenaar P" first="Patrick A" last="Degenaar">Patrick A. Degenaar</name><affiliation wicri:level="1"><nlm:aff id="I3">School of Electrical, Electronic and Computer Engineering, Newcastle University, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Electrical, Electronic and Computer Engineering, Newcastle University</wicri:regionArea><wicri:noRegion>Newcastle University</wicri:noRegion></affiliation></author></analytic><series><title level="j">BioMedical Engineering OnLine</title><idno type="eISSN">1475-925X</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>In this paper we present a novel scene retargeting technique to reduce the visual scene while maintaining the size of the key features. The algorithm is scalable to implementation onto portable devices, and thus, has potential for augmented reality systems to provide visual support for those with tunnel vision. We therefore test the efficacy of our algorithm on shrinking the visual scene into the remaining field of view for those patients.</p></sec><sec><title>Methods</title><p>Simple spatial compression of visual scenes makes objects appear further away. We have therefore developed an algorithm which removes low importance information, maintaining the size of the significant features. Previous approaches in this field have included <italic>seam carving</italic>, which removes low importance seams from the scene, and <italic>shrinkability </italic>which dynamically shrinks the scene according to a generated importance map. The former method causes significant artifacts and the latter is inefficient. In this work we have developed a new algorithm, combining the best aspects of both these two previous methods. In particular, our approach is to generate a <italic>shrinkability </italic>importance map using as seam based approach. We then use it to dynamically shrink the scene in similar fashion to the <italic>shrinkability </italic>method. Importantly, we have implemented it so that it can be used in real time without prior knowledge of future frames.</p></sec><sec><title>Results</title><p>We have evaluated and compared our algorithm to the <italic>seam carving </italic>and image <italic>shrinkability </italic>approaches from a content preservation perspective and a compression quality perspective. Also our technique has been evaluated and tested on a trial included 20 participants with simulated tunnel vision. Results show the robustness of our method at reducing scenes up to 50% with minimal distortion. We also demonstrate efficacy in its use for those with simulated tunnel vision of 22 degrees of field of view or less.</p></sec><sec><title>Conclusions</title><p>Our approach allows us to perform content aware video resizing in real time using only information from previous frames to avoid jitter. Also our method has a great benefit over the ordinary resizing method and even over other image retargeting methods. We show that the benefit derived from this algorithm is significant to patients with fields of view 20° or less.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Foster, A" uniqKey="Foster A">A Foster</name></author><author><name sortKey="Resnikoff, S" uniqKey="Resnikoff S">S Resnikoff</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hamel, C" uniqKey="Hamel C">C Hamel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vargas Martin, F" uniqKey="Vargas Martin F">F Vargas-Martín</name></author><author><name sortKey="Pelaez Coca, Md" uniqKey="Pelaez Coca M">MD Peláez-Coca</name></author><author><name sortKey="Ros, E" uniqKey="Ros E">E Ros</name></author><author><name sortKey="Diaz, J" uniqKey="Diaz J">J Diaz</name></author><author><name sortKey="Mota, S" uniqKey="Mota S">S Mota</name></author></analytic></biblStruct><biblStruct><analytic><author><name 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Degenaar</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Royaume-Uni</li><li>Égypte</li></country><region><li>Angleterre</li><li>Grand Londres</li></region><settlement><li>Londres</li></settlement></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Al Atabany, Walid I" sort="Al Atabany, Walid I" uniqKey="Al Atabany W" first="Walid I" last="Al-Atabany">Walid I. Al-Atabany</name></region><name sortKey="Degenaar, Patrick A" sort="Degenaar, Patrick A" uniqKey="Degenaar P" first="Patrick A" last="Degenaar">Patrick A. Degenaar</name><name sortKey="Tong, Tzyy" sort="Tong, Tzyy" uniqKey="Tong T" first="Tzyy" last="Tong">Tzyy Tong</name></country><country name="Égypte"><noRegion><name sortKey="Al Atabany, Walid I" sort="Al Atabany, Walid I" uniqKey="Al Atabany W" first="Walid I" last="Al-Atabany">Walid I. Al-Atabany</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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