A support system for maintenance training by augmented reality

We are developing a support system for maintenance training of electric power facilities by augmented reality. A user of the system wears a head-mounted display (HMD), with a small camera and with a sensor on top of the head, and stands in front of a blue screen. The system recognizes an object and estimates its position and pose from an image that is captured by the small camera. The object recognition and the position-pose estimation are performed by image processing without any special image marks or sensors. After the object recognition and the position-pose estimation, the system overlays computer graphics on the object images. The computer graphics show operational guidance, instructions, or dynamic inside movements of the object on the HMD. We describe an outline of the system and evaluation results of the system functions: the object recognition, the position-pose estimation and the operational guidance.

[1]  Katashi Nagao,et al.  The world through the computer: computer augmented interaction with real world environments , 1995, UIST '95.

[2]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[3]  Steven K. Feiner,et al.  A touring machine: Prototyping 3D mobile augmented reality systems for exploring the urban environment , 1997, Digest of Papers. First International Symposium on Wearable Computers.

[4]  Steven K. Feiner,et al.  A touring machine: Prototyping 3D mobile augmented reality systems for exploring the urban environment , 1997, Digest of Papers. First International Symposium on Wearable Computers.

[5]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[7]  Tomaso A. Poggio,et al.  Trainable pedestrian detection , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[8]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[9]  People Recognition in Image Sequences by Supervised Learning , 2000 .

[10]  Tomaso A. Poggio,et al.  Object recognition and detection by a combination of support vector machine and rotation invariant phase only correlation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[11]  Massimiliano Pontil,et al.  Face Detection in Still Gray Images , 2000 .

[12]  Tomaso A. Poggio,et al.  People recognition and pose estimation in image sequences , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[13]  Fujio Tsutsumi,et al.  Hybrid approach of video indexing and machine learning for rapid indexing and highly precise object recognition , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[14]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Massimiliano Pontil,et al.  Maintenance Training of Electric Power Facilities Using Object Recognition by SVM , 2002, SVM.

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.