Temporal accumulation of oriented visual features

In this paper we present a framework for accumulating on-line a model of a moving object (e.g., when manipulated by a robot). The proposed scheme is based on Bayesian filtering of local features, filtering jointly position, orientation and appearance information. The work presented here is novel in two aspects: first, we use an estimation mechanism that updates iteratively not only geometrical information, but also appearance information. Second, we propose a probabilistic version of the classical n-scan criterion that allows us to select which features are preserved and which are discarded, while making use of the available uncertainty model. The accumulated representations have been used in three different contexts: pose estimation, robotic grasping, and driver assistance scenario

Object Action Complexes as an Interface for Planning and Robot Control

Abstract — Much prior work in integrating high-level artificial intelligence planning technology with low-level robotic control has foundered on the significant representational differences between these two areas of research. We discuss a proposed solution to this representational discontinuity in the form of object-action complexes (OACs). The pairing of actions and objects in a single interface representation captures the needs of both reasoning levels, and will enable machine learning of high-level action representations from low-level control representations. I. Introduction and Background The different representations that are effective for continuous control of robotic systems and the discrete symbolic AI presents a significant challenge for integrating AI planning research and robotics. These areas of research should be abl

Bootstrapping Relational Affordances of Object Pairs using Transfer

This work was supported in part by the U.K. EPSRC DTG EP/J5000343/1 at Aberdeen, and in part by the EU Cognitive Systems Project XPERIENCE at SDU under Grant FP7-ICT-270273.Peer reviewedPostprin

Multi-View Object Instance Recognition in an Industrial Context

We present a fast object recognition system coding shape by viewpoint invariant geometric relations and appearance information. In our advanced industrial work-cell, the system can observe the work space of the robot by three pairs of Kinect and stereo cameras allowing for reliable and complete object information. From these sensors, we derive global viewpoint invariant shape features and robust color features making use of color normalization techniques. We show that in such a set-up, our system can achieve high performance already with a very low number of training samples, which is crucial for user acceptance and that the use of multiple views is crucial for performance. This indicates that our approach can be used in controlled but realistic industrial contexts that require—besides high reliability—fast processing and an intuitive and easy use at the end-user side.European UnionDanish Council for Strategic Researc

Multi-View Object Instance Recognition in an Industrial Context

We present a fast object recognition system coding shape by viewpoint invariant geometric relations and appearance information. In our advanced industrial work-cell, the system can observe the work space of the robot by three pairs of Kinect and stereo cameras allowing for reliable and complete object information. From these sensors, we derive global viewpoint invariant shape features and robust color features making use of color normalization techniques. We show that in such a set-up, our system can achieve high performance already with a very low number of training samples, which is crucial for user acceptance and that the use of multiple views is crucial for performance. This indicates that our approach can be used in controlled but realistic industrial contexts that require—besides high reliability—fast processing and an intuitive and easy use at the end-user side.European UnionDanish Council for Strategic Researc

Face Recognition and Gender Determination

The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces