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dc.contributor.authorRajakaruna, Rajakuruna
dc.contributor.authorRathnayake, Rathnayake
dc.contributor.authorChan, Kit Yan
dc.contributor.authorMurray, Iain
dc.contributor.editorIEEE
dc.date.accessioned2017-01-30T14:58:51Z
dc.date.available2017-01-30T14:58:51Z
dc.date.created2014-05-13T20:00:36Z
dc.date.issued2014
dc.identifier.citationRajakaruna, N. and Rathnayake, C. and Chan, K.Y. and Murray, I. 2014. Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data, in IEEE (ed), Proceedings of the Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Apr 21-24, pp. 1-6. Singapore: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/42321
dc.identifier.doi10.1109/ISSNIP.2014.6827599
dc.description.abstract

Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes blurring, as the camera is continuously moving by the body movement. This paper proposes a deblurring methodology that takes advantage of the fact that most smartphones are equipped with 3-axis accelerometers and gyroscopes. It uses data of the accelerometer and gyroscope to derive a motion vector calculated from the motion of the smartphone during the image-capturing period. A heuristic method, namely particle swarm optimization, is developed to determine the optimal motion vector, in order to deblur the captured image by reversing the effect of motion. Experimental results indicated that deblurring can be successfully performed using the optimal motion vector and that the deblurred images can be used as a readily approach to object and path identification in vision based navigation systems, especially for blind and vision impaired indoor/outdoor navigation. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. This experiment aims to identify issues in image quality including low light conditions, low quality images due to movement of the capture device and static and moving obstacles in front of the user in both indoor and outdoor environments. From this information, image-processing techniques to will be identified to assist in object and path edge detection necessary to create a guidance system for those with low vision.

dc.publisherIEEE
dc.subjectparticle swarm optimization
dc.subjectvision impaired navigation
dc.subjectinertial sensors
dc.subjectimage deblurring
dc.titleImage Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data
dc.typeConference Paper
dcterms.source.startPage1
dcterms.source.endPage6
dcterms.source.titleProceeding of the IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)
dcterms.source.seriesProceeding of the IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)
dcterms.source.isbn978-1-4799-2843-9
dcterms.source.conference2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)
dcterms.source.conference-start-dateApr 21 2014
dcterms.source.conferencelocationSingapore
dcterms.source.placeUSA
curtin.note

Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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curtin.accessStatusOpen access


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