Takaharu Kouda

Tactile texture recognition using convolutional neural networks for time-series data of pressure and 6-axis acceleration sensor

Tactile texture is an important factor to determine to impression of an object. To measure a feeling of an object, a numerical evaluation method for tactile texture is required. In this paper, we propose a novel recognition method for tactile texture using deep convolutional neural networks. In proposed framework, the tactile texture information is obtained by analyzing a time-series data of a pressure sensor and 6-axis acceleration sensor. Thus, the system configuration is simple, and it is possible to construct the system inexpensively.

Color conversion algorithm for color blindness using self-organizing map

The symptoms of “color blindness” are due to an innate lack or deficit of cone cells that recognize colors. People with color blindness have difficulty discriminating combinations of specific colors. In this paper, we propose a novel color conversion algorithm for color blindness. In the proposed method, the difficult colors for discrimination by color blindness are detected and converted for the legibility. This color conversion is consist of a color clustering using self-organizing map (SOM) and a analysis of the map structure. To validate the effectiveness of proposed method, it is applied to a image have various color combinations.