Recently, deep learning-based image matting methods have emerged. However, the existing methods lack the capability to provide precise matting for anime-style illustrations because their network parameters are trained on primarily photo-realistic images. In this paper, we introduces a new anime image dataset, Chara-1M, designed for matting purposes. In addition, we propose AniCropify, a new matting method for character anime images. Focusing on the commonalities of representation between anime images and photo-realistic images, in AniCropify, an anime image is first converted into a photo-realistic image. From the converted image, a trimap is generated to identify the human regions in images. By using the trimap in the matting process, precise alpha masks of anime images can be obtained. From experiments, we confirmed that based on the quality evaluation of matting results, the proposed method received the highest rating compared to other state-of-the-art techniques.

In wearable computing environments, users acquire visual information in various scenes using a head mounted display (HMD). However, this induces problems due to their differences from conventional displays such as smartphone and e-books. In this research, we focused on the problem of vertical shock caused by walking. This problem interferes with seeing information on HMDs. In this paper, we discuss selection of font shapes to minimize the effect of this problem. If we can clarify the characteristics of the readability of fonts in wearable computing environments, the application designers can select fonts that strike a balance between intended design elements and readability. In this paper, we first investigated the characteristics of fonts used in Japan considering the HMD swing that results from walking, from the viewpoints of readability (text readability) and legibility (ease of letter recognition) using six different fonts. From this evaluation, we find that fonts with very thin horizontal lines and with very thin horizontal and vertical lines should not be presented on HMDs.