Daido University

About: Daido University is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Ultimate tensile strength & Proton exchange membrane fuel cell. The organization has 209 authors who have published 423 publications receiving 3223 citations.

System Supporting Independent Walking of the Visually Impaired

Abstract: This paper proposes an integrated navigation system that supports the independent walking of the visually impaired. It supports route guidance and adjustments, zebra-crossing detection and guidance, pedestrian traffic signal detection and discrimination, and localization of the entrance doors of the buildings of destinations. This system was implemented on an Android smartphone. As a result of experiments, our system’s detection rate for zebra crossings was about 72 % and about 80 % for traffic signals.

Lindelöf theorems for monotone Sobolev functions in Orlicz spaces on uniform domains

Abstract: In this paper, we are concerned with Lindelof type theorems for monotone (in the sense of Lebesgue) Sobolev functions u on a uniform domain D⊂Rn satisfying ∫D|∇u(z)|n−1φ(|∇u(z)|)ωδD(z)dz

Experimental Evaluation of Multi-key Content-Based Image Retrieval

Abstract: A user of existing Content-Based Image Retrieval (CBIR) systems suffers from single query key selection, in case that he/she might begin retrieval process toward an ambiguous goal. In the other case, he/she cannot judge whether one image is more adequate than the other exactly for retrieval. To overcome the difficulty, this paper proposes Multi-Key CBIR (MK-CBIR) which differs from Single-Key CBIR (SK-CBIR) in retrieving similar images from image databases with a multi-key query, not a single-key one. To implement MK-CBIR, skyline query based MK-CBIR (\(\Phi _{sky}\)) and aggregate k-Nearest Neighbor query based MK-CBIR (\(\Omega _{sum}\), \(\Omega _{max}\), and \(\Omega _{min}\)) are presented. Experimental results on retrieval performance of MK-CBIR are as follows: Recall of \(\Omega _{max}\) is over 0.3 higher than that of k-NN search and the highest among those of MK-CBIR. It is comparatively stable against noisy keys. Recall of \(\Omega _{min}\) is roughly the same as that of k-NN search and the lowest among those of MK-CBIR. Recall of \(\Omega _{sum}\) and recall of \(\Phi _{sky}\) are roughly the same and between those of \(\Omega _{min}\) and \(\Omega _{max}\).