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dc.contributor.authorHuang, Chao-Huien_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-08T15:06:58Z-
dc.date.available2014-12-08T15:06:58Z-
dc.date.issued2007-01-01en_US
dc.identifier.issn1057-7122en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TCSI.2006.887975en_US
dc.identifier.urihttp://hdl.handle.net/11536/5445-
dc.description.abstractFor decades, numerous scientists have examined the following questions: "How do humans see the world?" and "How do humans experience vision?" To answer these questions, this study proposes a computer fovea model based on hexagonal-type cellular neural network (hCNN). Certain biological mechanisms of a retina can be simulated using an in-state-of-art architecture named CNN. Those biological mechanisms include the behaviors of the photoreceptors, horizontal cells, ganglions, and bipolar cells, and their co-operations in the retina. Through investigating the model and the abilities of the CNN, various properties of the human vision system can be simulated. The human visual system possesses numerous interesting properties, which provide natural methods of enhancing visual information. Various visual information enhancing algorithms can be developed using these properties and the proposed model. The proposed algorithms include color constancy, image sharpness, and some others. This study also discusses how the proposed model works for video enhancement and demonstrates it experimentally.en_US
dc.language.isoen_USen_US
dc.subjectbipolar cellen_US
dc.subjectcellular neural networks (CNNs)en_US
dc.subjectcolor constancyen_US
dc.subjectfoveaen_US
dc.subjectganglionen_US
dc.subjecthexagonalen_US
dc.subjecthorizontal cellen_US
dc.subjectphotoreceptoren_US
dc.subjectretinaen_US
dc.subjectsharpnessen_US
dc.titleBio-inspired computer fovea model based on hexagonal-type cellular neural networken_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.doi10.1109/TCSI.2006.887975en_US
dc.identifier.journalIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERSen_US
dc.citation.volume54en_US
dc.citation.issue1en_US
dc.citation.spage35en_US
dc.citation.epage47en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000243915200005-
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