Artificial Intelligence
Active Computer Vision by Cooperative Focus and Stereo
Active Computer Vision by Cooperative Focus and Stereo
The 4th International Symposium on Experimental Robotics IV
Integrating Face Recognition into Security Systems
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Cooperative Vision in a Multi-Agent Architecture
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
The Mobile Robot Rhino
A probabilistic model of overt visual attention for cognitive robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model-based recognition of domino tiles using TGraphs
Proceedings of the 32nd DAGM conference on Pattern recognition
Model-based recognition of 2D objects under perspective distortion
Pattern Recognition and Image Analysis
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We introduce a mobile robot playing at dominoes. The robot is equipped with a pan-tilt unit and a CCD camera and is solely guided by the vision system. Besides the mobile robot the overall system is distributed on a general-purpose workstation. The communication is realized by three radio links transmitting the video signal and two serial data streams for the pan-tilt and the robot controller respectively. Two client/server connections enable a bidirectional data exchange between the image processing software and the robot control and navigation software. The protocol includes requests for certain actions to be performed by the opposite module and the transmission of domino coordinates. The robot identifies the dominoes, collects them, and deposits them in form of a straight chain where two adjacent domino halves possess the same digit. Several iterations of a perception action cycle have to be executed to fulfill this task: The first cycle starts with a visual exploration of the robot's environment and a subsequent explicit foveation of detected domino point clusters. After a coordinate transformation the dominoes are identified by template matching followed by a comparison with a knowledge base to eliminate ambiguities. The positions of the dominoes are registered into the global map space of the robot which position is continuously updated while it is moving. When the robot has reached a so-called virtual point in front of a specified domino the robot requests a control view towards this domino to verify its position. After feedback from the vision system the robot grips the target, moves to the appropriate position in front of the already formed chain, again verifies its position, and deposits the domino. Afterwards the robot returns to its initial position where the cycle starts again with the selection of the next target domino.