Browsing by Author "Hadjileontiadis, Leontios"
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- Fiducials Marks Detection to Assist Visually Impaired People NavigationPublication . Costa, Paulo; Fernandes, Hugo; Vasconcelos, Veronica; Coelho, Paulo; Barroso, Joao; Hadjileontiadis, LeontiosAssistive technology enables people to achieve independence in the accomplishment of their daily tasks and enhance their quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of information or given unsufficient information about their surrounding environment. With the recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities during their mobility. In this context we propose and describe the SmartVision project, whose global objective is to assist visually impaired people in their navigation through unknown indoor and outdoor environments. This paper is focused mainly on the Computer Vision module of the SmartVision prototype, were we propose a new algorithm to recognise fiducials marks suitably placed on sidewalks, revealing to be a promising solution.
- Landmarks Detection to Assist the Navigation of Visually Impaired PeoplePublication . Costa, Paulo; Fernandes, Hugo; Vasconcelos, Verónica; Coelho, Paulo; Barroso, João; Hadjileontiadis, LeontiosAssistive technology enables people to achieve independence in the accomplishment of their daily tasks and enhance their quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of information or given insufficient information about their surrounding environment. With the recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities during their mobility. In this context we propose a new algorithm to recognize landmarks suitably placed on sidewalks. The proposed algorithm uses a combination of Peano-Hilbert Space Filling Curves for dimension reduction of image data and Ensemble Empirical Mode Decomposition (EEMD) to pre-process the image, resulting on a fast and efficient recognition method and revealing a promising solution.