Statistical Shape Feedback for Human Subject Segmentation

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/08/01

タイトル(英語): Statistical Shape Feedback for Human Subject Segmentation

著者名: Esmaeil Pourjam (Graduate School of Information Science, Nagoya University), Daisuke Deguchi (Information Strategy Office, Nagoya University), Ichiro Ide (Graduate School of Information Science, Nagoya University), Hiroshi Murase (Graduate School of Infor

著者名(英語): Esmaeil Pourjam (Graduate School of Information Science, Nagoya University), Daisuke Deguchi (Information Strategy Office, Nagoya University), Ichiro Ide (Graduate School of Information Science, Nagoya University), Hiroshi Murase (Graduate School of Information Science, Nagoya University)

キーワード: Human Segmentation，Grab-cut，Statistical Shape Model

要約(英語): Human segmentation is one of the most interesting yet most challenging subjects in the field of object segmentation and image processing. It can be used in various types of applications from image retrieval to robotics and human machine interfaces, including even entertainment. Many researches have been done on this subject and it is still one of active research areas. But until now, a method for accurate segmentation in different conditions has not been introduced. In this paper, we present “Statistical Shape Feedback Segmentation” (SSFSeg) method, which is a way to automatically segment human subjects (pedestrians) from single images. Our main contributions in this paper are: 1) Using human shape model as priors for Grab-cut segmentation. 2) Implementation of a feedback system which provides a coarse-to-fine way of generating more accurate shapes. For this task, we try to use masks generated by the Statistical Shape Model (SSM) algorithm as a prior input for the Grab-cut technique to segment the desired human subject in the image without user interaction. To achieve this, we propose a feedback framework for the SSM sample generation. Our experiments confirmed that the segmentation error of our proposed method is less than half of the Grab-cut method.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.8 （2015） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 1000-1008 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/8/135_1000/_article/-char/ja/