Title:Using Recognition to Guide a Robot's Attention
Authors: Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars and Luc Van Gool
Abstract: In the transition from industrial to service robotics,
robots will have to deal with increasingly unpredictable and
variable environments. We present a system that is able to
recognize objects of a certain class in an image and to identify
their parts for potential interactions. This is demonstrated for
object instances that have never been observed during training,
and under partial occlusion and against cluttered backgrounds.
Our approach builds on the Implicit Shape Model of Leibe and
Schiele, and extends it to couple recognition to the provision of
meta-data useful for a task. Meta-data can for example consist of
part labels or depth estimates. We present experimental results
on wheelchairs and cars.
robots will have to deal with increasingly unpredictable and
variable environments. We present a system that is able to
recognize objects of a certain class in an image and to identify
their parts for potential interactions. This is demonstrated for
object instances that have never been observed during training,
and under partial occlusion and against cluttered backgrounds.
Our approach builds on the Implicit Shape Model of Leibe and
Schiele, and extends it to couple recognition to the provision of
meta-data useful for a task. Meta-data can for example consist of
part labels or depth estimates. We present experimental results
on wheelchairs and cars.
RSS Online Proceedings: here
Abstract: here
PDF: here
Their work first use object recognition to recognize the whole objects, and then use segmentation to better localize parts.
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