3 research outputs found
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