This paper addresses the attribute recognition problem, a field of research that is dominated by studies in the visible spectrum. Only a few works are available in the thermal spectrum, which is fundamentally different from the visible one. This research performs recognition specifically on wearable attributes, such as glasses and masks. Usually these attributes are relatively small in size when compared with the human body, on top of a large intra-class variation of the human body itself, therefore recognizing them is not an easy task. Our method utilizes a decomposition framework based on Robust Principal Component Analysis (RPCA) to extract the attribute information for recognition. However, because it is difficult to separate the body and the attributes without any prior knowledge, noise is also extracted along with attributes, hampering the recognition capability. We made use of prior knowledge; namely the location where the attribute is likely to be present. The knowledge is referred to as the Probability Map, incorporated as a weight in the decomposition by RPCA. Using the Probability Map, we achieve an attribute-wise decomposition. The results show a significant improvement with this approach compared to the baseline, and the proposed method achieved the highest performance in average with a 0.83 F-score.

For people with serious disability, it is most significant to be able to use the same communication methods, for instance a telephone and an electronic mail system (e-mail), as ordinary people do in order to get a normal life and communicate with other people for leading a social life. In particular, having communications access to an e-mail is a very effective method of communication that enables them to convey their intention to other people directly while at the same time keep their privacy. However, it takes them much time and effort to input an e-mail text on the computer. They also need much support by their attendants. From this point of view, we propose a multi-modal communication system that is composed of a voice recognizer, a pointing device, and a text composer. This system intend to improve the man-machine interface for people with physical disability. In this system, our voice recognition technology plays a key role in providing a good interface between disabled people and the personal computer. When generating e-mail contents, users access the database containing user keywords, and the guidance menu from which they select the appropriate word by voice. Our experimental results suggest that this communication system improves not only the time efficiency of text composition but also the readiness of disabled people to communicate with other people. In addition, our disabled subject on this paper is not able to move his body, legs and hands due to suffer from muscular dystrophy. And he is able to move only his fingers and speak command words with the assistance of a respirator.

We present a novel training method for recognizing traffic sign symbols. The symbol images captured by a car-mounted camera suffer from various forms of image degradation. To cope with degradations, similarly degraded images should be used as training data. Our method artificially generates such training data from original templates of traffic sign symbols. Degradation models and a GA-based algorithm that simulates actual captured images are established. The proposed method enables us to obtain training data of all categories without exhaustively collecting them. Experimental results show the effectiveness of the proposed method for traffic sign symbol recognition.