möglich sobald bei der ZB eingereicht worden ist.
Supervised classification for customized intraoperative augmented reality visualization.
In: 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (11th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2012, Atlanta, United States, 05. - 08. November 2012). Piscataway, NJ: IEEE, 2012. 311-312
In this paper, we present a fusion algorithm supplemented with appropriate visualization by selecting relevant information from different modalities in mixed and augmented reality (AR). This encompasses a learning based method upon relevance of information, defined by an expert, which ultimately enables confident interventional decisions based on mixed reality (MR) images. The performance of our developed fusion and tailored visualization techniques was evaluated by employing X-ray/optical images during surgery and validated qualitatively using a 5-point Likert scale. Our observations indicated that the proposed technique provided semantic contextual information about underlying pixels and in general was preferred over the traditional pixel-wise linear alpha-blending method.
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Camc ; Fusion ; Medical Augmented Reality ; Relevant Information ; Visualization ; X-ray
Konferenztitel 11th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2012, Atlanta, United States, 05. - 08. November 2012
Konferenzband 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
Quellenangaben Seiten: 311-312
Verlagsort Piscataway, NJ
Institut(e) Institute of Computational Biology (ICB)