Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 540636
Title A Cloud-Based Big Data System to Support Visually Impaired People
Author(s) Temuçin, H.; Keçeli, A.S.; Kaya, A.; Yaliç, H.Y.; Tekinerdogan, B.
Source In: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications / Kumar Sangaja, A., Zhang, Z., Sheng, M., Elsevier Academic Press - ISBN 9780128133149 - 1 p.
Department(s) Information Technology
WASS
Publication type Peer reviewed book chapter
Publication year 2018
Abstract In society, visual impairment is one of the important health issues that severely impede the daily life and welfare of many people. According to the 2014 World Health Organization (WHO) report, there exist 285 million visually impaired people worldwide, and more than 400 thousand in Turkey. To support the visually impaired people and likewise help them integrate into the society, several challenges need to be solved. In this study, we focus on two important issues, including the reading of normal, non-braille text, and face recognition. Reading of normal texts beyond Braille is one of the important life activities that is required in the daily personal and professional life of people. Face recognition is important for social interaction and communication. To solve both problems we propose a system which can help visually impaired people to recognize human faces and read normal text. The tool is based on a cloud-based architecture whereby services are provided for text and face recognition. The services are based on big data analytics together with deep learning algorithms. In this chapter, we discuss the overall architecture for such a text and face recognition system, the design decisions, the key challenges, the presented analytics approaches and the lessons learned that could be of value to both practitioners and researchers.
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