Hierarchical Salient Object Detection for Assisted Grasping

Visual scene decomposition into semantic entities is one of the major challenges when creating a reliable object grasping system. Recently, we introduced a bottom-up hierarchical clustering approach which is able to segment objects and parts in a scene. In this paper, we introduce a transform from such a segmentation into a corresponding, hierarchical saliency function. In comprehensive experiments we demonstrate its ability to detect salient objects in a scene. Furthermore, this hierarchical saliency defines a most salient corresponding region (scale) for every point in an image. Based on this, an easy-to-use pick and place manipulation system was developed and tested exemplarily.Comment: Accepted for ICRA 201

Künstliche Intelligenz - Leitvorstellungen und Verantwortbarkeit

Cremers AB, Seetzen J, Wachsmuth I, eds. Künstliche Intelligenz - Leitvorstellungen und Verantwortbarkeit. VDI Report 21. Vol Band 2: Tagungsbericht. Düsseldorf: Verein Deutscher Ingenieure VDI; 1994

Künstliche Intelligenz - Leitvorstellungen und Verantwortbarkeit

Cremers AB, Haberbeck R, Seetzen J, Wachsmuth I, eds. Künstliche Intelligenz - Leitvorstellungen und Verantwortbarkeit. VDI Report 17. Düsseldorf: Verein Deutscher Ingenieure VDI; 1992