Consideration on performance improvement of shadow and reflection removal based on GMM
Kiyoshi Nishikawa, Yoshihiro Yamashita, Toru Yamaguchi, Takao Nishitani · 2016
Wearable devices are expected to provide a ubiquitous network connection in the near future. In this paper, we consider systems which uses human finger gestures as an input device. To assure accurate input characteristics, the shape of arm and fingers should be captured clearly, and for that purpose we consider using the Gaussian mixture model (GMM) foreground segmentation. It is known that shadow or reflection in the frame image affects the performance of GMM foreground segmentation. A low computational shadow or reflection removal methods are proposed [1]-[3] which are suitable to be implemented in wearable devices. Although the methods improve the foreground segmentation performance, the results depend on the characteristics of the video. In this paper, we consider improving the performance of the methods by modifying the equation for deciding the shadow region. Through the computer simulations, we show the effectiveness of the proposed method.