Learning, tracking and recognition of 3D objects

In this contribution we describe steps towards the implementation of an active robot vision system. In a sequence of images taken by a camera mounted on the hand of a robot, we detect, track, and estimate the position and orientation (pose) of a three-dimensional moving object. The extraction of the...

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Vydáno v:IROS '94 : proceedings of the IEEE/RSJ/GI international conference on intelligent robots and systems : advanced robotic systems and the real world, September 12-16, 1994, Federal Armed Forces University, Munich, Germany Ročník 1; s. 89 - 96 vol.1
Hlavní autoři: Denzler, J., Bess, R., Hornegger, J., Niemann, H., Paulus, D.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 1994
Témata:
Calibration
Cameras
Grippers
Image motion analysis
Image sequence analysis
Layout
Object detection
Robot vision systems
Robotics and automation
Tracking
ISBN:9780780319332, 0780319338
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Abstract In this contribution we describe steps towards the implementation of an active robot vision system. In a sequence of images taken by a camera mounted on the hand of a robot, we detect, track, and estimate the position and orientation (pose) of a three-dimensional moving object. The extraction of the region of interest is done automatically by a motion tracking step. For learning 3-D objects using two-dimensional views and estimating the object's pose, a uniform statistical method is presented which is based on the expectation-maximization-algorithm (EM-algorithm). An explicit matching between features of several views is not necessary. The acquisition of the training sequence required for the statistical learning process needs the correlation between the image of an object and its pose; this is performed automatically by the robot. The robot's camera parameters are determined by a hand/eye-calibration and a subsequent computation of the camera position using the robot position. During the motion estimation stage the moving object is computed using active, elastic contours (snakes). We introduce a new approach for online initializing the snake on the first images of the given sequence, and show that the method of snakes is suited for real time motion tracking.< >
AbstractList In this contribution we describe steps towards the implementation of an active robot vision system. In a sequence of images taken by a camera mounted on the hand of a robot, we detect, track, and estimate the position and orientation (pose) of a three-dimensional moving object. The extraction of the region of interest is done automatically by a motion tracking step. For learning 3-D objects using two-dimensional views and estimating the object's pose, a uniform statistical method is presented which is based on the expectation-maximization-algorithm (EM-algorithm). An explicit matching between features of several views is not necessary. The acquisition of the training sequence required for the statistical learning process needs the correlation between the image of an object and its pose; this is performed automatically by the robot. The robot's camera parameters are determined by a hand/eye-calibration and a subsequent computation of the camera position using the robot position. During the motion estimation stage the moving object is computed using active, elastic contours (snakes). We introduce a new approach for online initializing the snake on the first images of the given sequence, and show that the method of snakes is suited for real time motion tracking.< >
Author Denzler, J.
Niemann, H.
Hornegger, J.
Bess, R.
Paulus, D.
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PublicationTitle IROS '94 : proceedings of the IEEE/RSJ/GI international conference on intelligent robots and systems : advanced robotic systems and the real world, September 12-16, 1994, Federal Armed Forces University, Munich, Germany
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Snippet In this contribution we describe steps towards the implementation of an active robot vision system. In a sequence of images taken by a camera mounted on the...
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StartPage 89
SubjectTerms Calibration
Cameras
Grippers
Image motion analysis
Image sequence analysis
Layout
Object detection
Robot vision systems
Robotics and automation
Tracking
Title Learning, tracking and recognition of 3D objects
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