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Bin YAO Hua WU Yun YANG Yuyan CHAO Atsushi OHTA Haruki KAWANAKA Lifeng HE
The Euler number of a binary image is an important topological property for pattern recognition, and can be calculated by counting certain bit-quads in the image. This paper proposes an efficient strategy for improving the bit-quad-based Euler number computing algorithm. By use of the information obtained when processing the previous bit quad, the number of times that pixels must be checked in processing a bit quad decreases from 4 to 2. Experiments demonstrate that an algorithm with our strategy significantly outperforms conventional Euler number computing algorithms.
Koji OGURI Haruki KAWANAKA Shintaro ONO
The environment surrounding automotive technology is undergoing a major transformation. In particular, as technological innovation advances in new areas called “CASE” such as Connected, Autonomous/Automated, Shared, and Electric, various research activities are underway. However, this is an approach from the standpoint of the automobile centered, and when considering the development of a new automobile society, it is necessary to consider from the standpoint of “human centered,” who are users, too. Therefore, this paper proposes the possibility of technological innovation in the area of “Another CASE” such as Comfortable, Accessible, Safety, and Enjoy/Exciting, and introduces the contents of some interesting researches.
Yasuhiko NAKANO Haruki KAWANAKA Koji OGURI
This study explored the question of how to minimize older drivers' accidents and to identify at-risk drivers by analyzing their driving performance. Previous traffic research reported that there were two factors involved in risky driving, namely driving risk perception and risky driving attitude. We investigated these two factors as indicators of an at-risk driver by using large-scale test data from license renewal tests that are obligatory for Japanese drivers who are 70 years of age or older. The tests include a driving simulator test, an on-road test, and a cognitive screening test. By using these assessments and predictions made with renewal driving tests, we were able to indicate the possibility of identifying at-risk drivers.