Recently, dense trajectories  have been shown to be a successful video representation for action recognition, and have demonstrated state-of-the-art results with a variety of datasets. However, if we apply these trajectories to gesture recognition, recognizing similar and fine-grained motions is problematic. In this paper, we propose a new method in which dense trajectories are calculated in segmented regions around detected human body parts. Spatial segmentation is achieved by body part detection . Temporal segmentation is performed for a fixed number of video frames. The proposed method removes background video noise and can recognize similar and fine-grained motions. Only a few video datasets are available for gesture classification; therefore, we have constructed a new gesture dataset and evaluated the proposed method using this dataset. The experimental results show that the proposed method outperforms the original dense trajectories.
The high prevalence of cataracts is still a serious public health problem as a leading cause of blindness, especially in
developing countries with limited health facilities. In this paper we propose a new screening method for cataract
diagnosis by easy-to-use and low cost imaging equipment such as commercially available digital cameras. The
difficulties in using this sort of digital camera equipment are seen in the observed images, the quality of which is not
sufficiently controlled; there is no control of illumination, for example. A sign of cataracts is a whitish color in the pupil
which usually is black, but it is difficult to automatically analyze color information under uncontrolled illumination
conditions. To cope with this problem, we analyze specular reflection in the pupil region. When an illumination light
hits the pupil, it makes a specular reflection on the frontal surface of the lens of the pupil area. Also the light goes
through the rear side of the lens and might be reflected again. Specular reflection always appears brighter than the
surrounding area and is also independent of the illumination condition, so this characteristic enables us to screen out
serious cataract robustly by analyzing reflections observed in the eye image. In this paper, we demonstrate the validity
of our method through theoretical discussion and experimental results. By following the simple guidelines shown in this
paper, anyone would be able to screen for cataracts.