Point Estimation with Markers for Effective Mobile Auditory Graphs

Abstract

While researchers have performed numerous studies to understand the human interpretation of visual graphs in reading, comprehending and interpreting displayed data; visually impaired (VI) students still face many challenges that prevent them from fully benefiting from these graphs in class. In this study, we conducted a test with 20 students to track the work described in studies in an expanded scenario. As we have tried to answer the question as to whether adding multi-reference mapping of sonification to auditory graphs could improve the of point estimation accuracy in non-visual condition. We also emphasize the efficiency of the performance of multi-reference graphs to make them as efficient as mapping using single pitch. Our proposed study improved performance of multi-reference task completion time by having fewer reference note. The results help provide empirical evidence that the multi-reference mode provides more accurate results than the single-pitch mode and confirms that adding contexts to auditory graphs could be used for better comprehension.

Prediction of Drug Users Based on Facial Scratching Pattern

“…Facial recognition technology can identify drug addicts in biometric recognition systems, search and index digital image and video databases, including security sys-tems, video conferencing, and human interaction with computers. The face recognition classification method is generally divided into three [3], using local features, holistic features, and based on hybrid [4]- [7]. Local binary patterns and grey level of occurrence matrix have been widely used for feature extraction of facial recognition method [8] [9].…”