EyeR: Detection Support for Visually Impaired Users

Kirjoittajan mukaan tämä on kokonainen tieteellinen vertaisarvioitu artikkeli, vaikka kirjan nimessä lukeekin extended abstracts.Lack of adequate support in navigation and object detection can limit independence of visually impaired (VI) people in their daily routines. Common solutions include white canes and guide dogs. White canes are useful in object detection, but require physically touching objects with the cane, which may be undesired. Guide dogs allow navigation without touching objects in the vicinity, but cannot help in object detection. By addressing this gap, employing a user-centric research approach, we aim to find a solution to improve the independence of VI people. Here, we began by initially gathering requirements through online questionnaires. Working from this, we build a prototype of a glove that alerts its users when an obstacle is detected at the pointed position; we call this EyeR. Lastly, we evaluated EyeR with VI users and found out that in use our prototype provides real time feedback and is helpful in navigation. We also provide future recommendations for VI prototypes from our participants, who would additionally like the device to recognise objects.Peer reviewe

SVG-to-RDF Image Semantization

International audienceThe goal of this work is to provide an original (semi-automatic) annotation framework titled SVG-to-RDF whichconverts a collection of raw Scalable vector graphic (SVG) images into a searchable semantic-based RDF graph structure that encodes relevant features and contents. Using a dedicated knowledge base, SVG-to-RDF offers the user possible semantic annotations for each geometric object in the image, based on a combination of shape, color, and position similarity measures. Our method presents several advantages, namely i) achieving complete semantization of image content, ii) allowing semantic-based data search and processing using standard RDF technologies, iii) while being compliant with Web standards (i.e., SVG and RDF) in displaying images and annotation results in any standard Web browser, as well as iv) coping with different application domains. Our solution is of linear complexity in the size of the image and knowledge base structures used. Using our prototype SVG2RDF, several experiments have been conducted on a set of panoramic dental x-ray images to underline our approach’s effectiveness, and its applicability to different application domains