A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Tool for Constructing 3D Environments with Virtual Agents
Multimedia Tools and Applications
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
Supervised semantic labeling of places using information extracted from sensor data
Robotics and Autonomous Systems
An embedded HMM-based approach for face detection and recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
SRP Based Natural Interaction between Real and Virtual Worlds in Augmented Reality
CW '08 Proceedings of the 2008 International Conference on Cyberworlds
Relational object maps for mobile robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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In this paper, a method for automatically building 3D virtual worlds which correspond to the the objects detected in a real environment is presented. The proposed method can be used in many applications such as for example Virtual Reality, Augmented Reality, remote inspection and Virtual Worlds generation. Our method requires an operator equipped with a stereo camera and moving in an office environment. The operator takes a picture of the environment and, with the proposed method, the Regions of Interest (ROI) are extracted from each picture, their content is classified and 3D virtual scenarios are reconstructed using icons which resemble the classified object categories. ROI extraction, pose and height estimation of the classified objects are performed using stereo vision. The ROIs are obtained using a Dempster-Shafer technique for fusing different information detected from the image such as the Speeded Up Robust Features (SURF) and depth data obtained with the stereo camera. Experimental results are presented in office environments.