Exploring computer-generated line graphs through virtual touch

This paper describes the development and evaluation of a haptic interface designed to provide access to line graphs for blind or visually impaired people. Computer-generated line graphs can be felt by users through the sense of touch produced by a PHANToM force feedback device. Experiments have been conducted to test the effectiveness of this interface with both sighted and blind people. The results show that sighted and blind people have achieved about 89.95% and 86.83% correct answers respectively in the experiment

Design guidelines for audio presentation of graphs and tables

Audio can be used to make visualisations accessible to blind and visually impaired people. The MultiVis Project has carried out research into suitable methods for presenting graphs and tables to blind people through the use of both speech and non-speech audio. This paper presents guidelines extracted from this research. These guidelines will enable designers to implement visualisation systems for blind and visually impaired users, and will provide a framework for researchers wishing to investigate the audio presentation of more complex visualisations

Spatial audio in small display screen devices

Our work addresses the problem of (visual) clutter in mobile device interfaces. The solution we propose involves the translation of technique-from the graphical to the audio domain-for expliting space in information representation. This article presents an illustrative example in the form of a spatialisedaudio progress bar. In usability tests, participants performed background monitoring tasks significantly more accurately using this spatialised audio (a compared with a conventional visual) progress bar. Moreover, their performance in a simultaneously running, visually demanding foreground task was significantly improved in the eye-free monitoring condition. These results have important implications for the design of multi-tasking interfaces for mobile devices

The histogram of partitioned localized image textures

In the field of machine learning and pattern recognition, texture has been a prominent area of research. Humans are uniquely equipped to distinguish texture; however, computers are more equipped to automate the process. Computers accomplish this by taking images and extracting meaningful features that describe their texture. Some of these features are the Haralick texture features, local binary pattern (LBP), and the local direction pattern (LDP). Using the local directional pattern as an example, we propose a new texture feature called the histogram of partitioned localized image textures (HoPLIT). This feature utilizes a set of filters, not necessarily directional, and generates filter response vectors at every pixel location. These response vectors can be thought of as words in a document, which causes one to think of the bag-of-words model. Using the bag-of-words model, a codebook is created by partitioning a subset of response vectors from the entire data set. The partitions are represented by their mean texture and thus a word in the codebook. The mean textures now represent the keywords within the document, i.e. image. A histogram descriptor for an image is the frequency of pixels that belong to each partition. This feature is applied to a texture classification and segmentation problem as well as object detection. Within each problem domain, the HoPLIT feature is compared to the Haralick texture features, LBP, and LDP. The HoPLIT feature does very well classifying texture as well as segmenting large texture mosaics. HoPLIT also shows a surprising robustness to noise. Object detection proves to be slightly more difficult than texture classification for HoPLIT. However, it continues to outperform LBP and LDP.Field of study: Electrical and computer engineering.|James M. Keller, Ph.D., Thesis Supervisor.Includes bibliographical references (pages 54-58)