We propose a framework to aid a visually impaired user to recognize objects in an image by sonifying image edge fea- tures and distance-to-edge maps. Visually impaired people usually touch objects to recognize their shape. However, it is difficult to recognize objects printed on flat surfaces or ob- jects that can only be viewed from a distance, solely with our haptic senses. Our ultimate goal is to aid a visually impaired user to recognize basic object shapes, by transposing them to aural information. Our proposed method provides two types of image sonification: (1) local edge gradient sonifica- tion and (2) sonification of the distance to the closest image edge. Our method was implemented on a touch-panel mo- bile device, which allows the user to aurally explore image context by sliding his finger across the image on the touch screen. Preliminary experiments show that the combination of local edge gradient sonification and distance-to-edge soni- fication are effective for understanding basic line drawings. Furthermore, our tests show a significant improvement in image understanding with the introduction of proper user training.
- Tsubasa Yoshida, Kris M. Kitani, Hideki Koike, Serge Belongie, Kevin Schlei, EdgeSonic: Image Feature Sonification for the Visually Impaired, Proc. of the 2nd Augmented Human International Conference (AH'11), Article #11, 2011.