Real-time 3D Feature Extraction without Explicit 3D Object Reconstruction
For the communication between human and computer
in an interactive computing environment, the gesture recognition is
studied vigorously. Therefore, a lot of studies have proposed efficient
methods about the recognition algorithm using 2D camera captured
images. However, there is a limitation to these methods, such as the
extracted features cannot fully represent the object in real world.
Although many studies used 3D features instead of 2D features for
more accurate gesture recognition, the problem, such as the processing
time to generate 3D objects, is still unsolved in related researches.
Therefore we propose a method to extract the 3D features combined
with the 3D object reconstruction. This method uses the modified
GPU-based visual hull generation algorithm which disables unnecessary
processes, such as the texture calculation to generate three kinds
of 3D projection maps as the 3D feature: a nearest boundary, a farthest
boundary, and a thickness of the object projected on the base-plane. In
the section of experimental results, we present results of proposed
method on eight human postures: T shape, both hands up, right hand
up, left hand up, hands front, stand, sit and bend, and compare the
computational time of the proposed method with that of the previous
methods.
[1] J. Loffler, "Content-based retrieval of 3d models in distributed web
databases by visual shape information," in Proc. 4th International Conf.
Information Visualization, 2000, pp. 82.
[2] C.M. Cyr, B.B. Kimia, "A similarity-based aspect-graph approach to 3d
object recognition," International J. Computer Vision, vol. 57, 2004, pp.
5-22
[3] P. Min, J. Chen, T. Funkhouser, "A 2d sketch interface for a 3d model
search engine," in Proc. the International Conf. Computer Graphics and
Interactive Techniques, 2002, pp. 138.
[4] C. Chu, I. Cohen, "Posture and gesture recognition using 3d body shapes
decomposition," in Proc. the IEEE Computer Society Conf. CVPR, vol. 3,
2005, pp. 69.
[5] D. Kyoung, Y. Lee, W. Baek, E. Han, J. Yang, K. Jung, "Efficient 3d
voxel reconstruction using pre-computing method for gesture recognition,"
in Proc. Korea-Japan Joint Workshop, 2006, pp. 67-73.
[6] T. Funkhouser, P. Min, M. Kazhdan, J. Chen, A. Halderman, D. Dobkin,
D. Jacobs, "A search engine for 3d models," ACM Trans. Graphics, vol.
22, 2003, pp. 83-105.
[7] M. Hilaga, Y. Shinagawa, T. Kohmura, T. Kunii, "Topology matching
for fully automatic similarity estimation of 3d shapes." in Proc. the 28th
annual Conf. Computer graphics and interactive techniques, 2001, pp.
203-212.
[8] H. Sundar, D. Silver, N. Gagvani, S. Dickinson, "Skeleton based shape
matching and retrieval," in Proc. International Conf. Shape Modeling
International, 2003, pp. 130-139.
[9] N.D. Cornea, D. Silver, P. Min, "Curve-skeleton properties, applications
and algorithms," IEEE Trans. Visualization and Computer Graphics, vol.
13, 2007, pp. 530-548.
[10] N.D. Cornea, D. Silver, X. Yuan, R. Balasubramanian, "Computing
hierarchical curve-skeletons of 3d objects," in Proc. the Visual Computer,
vol. 21, 2005, pp. 945-955.
[11] A. Brennecke, T. Isenberg, "3d shape matching using skeleton graphs,"
in Proc. Simulation and Visualization, vol. 13, 2004, pp. 299-310
[12] A. Laurentini, "The visual hull concept for silhouette-based image
understanding," IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 16, 1994, pp. 150-162.
[13] M. Li, M. Magnor, H. Seidel, "Hardware-accelerated visual hull reconstruction
and rendering," in Proc. Graphics Interface, 2003, pp. 65-71.
[14] R. Szeliski, "Rapid octree construction from image sequences," in Proc.
CVGIP: Image Underst., vol. 58, 1993, pp. 23-32.
[15] W. Matusik, C. Buehler, L. McMillan, "Polyhedral visual hulls for
real-time rendering," in Proc. the 12th Eurographics Workshop. Rendering
Technique, 2001, pp. 115-126.
[16] C. Lee, J. Cho, K. Oh, "Hardware-accelerated jaggy-free visual hulls
with silhouette maps," in Proc. the ACM Sym. Virtual Reality Software
and Technology, 2006, pp. 87-90.
[17] C. Everitt, A. Rege, C. Cebenoyan, "Hardware shadow mapping,"
Technical report, NVIDIA.
[1] J. Loffler, "Content-based retrieval of 3d models in distributed web
databases by visual shape information," in Proc. 4th International Conf.
Information Visualization, 2000, pp. 82.
[2] C.M. Cyr, B.B. Kimia, "A similarity-based aspect-graph approach to 3d
object recognition," International J. Computer Vision, vol. 57, 2004, pp.
5-22
[3] P. Min, J. Chen, T. Funkhouser, "A 2d sketch interface for a 3d model
search engine," in Proc. the International Conf. Computer Graphics and
Interactive Techniques, 2002, pp. 138.
[4] C. Chu, I. Cohen, "Posture and gesture recognition using 3d body shapes
decomposition," in Proc. the IEEE Computer Society Conf. CVPR, vol. 3,
2005, pp. 69.
[5] D. Kyoung, Y. Lee, W. Baek, E. Han, J. Yang, K. Jung, "Efficient 3d
voxel reconstruction using pre-computing method for gesture recognition,"
in Proc. Korea-Japan Joint Workshop, 2006, pp. 67-73.
[6] T. Funkhouser, P. Min, M. Kazhdan, J. Chen, A. Halderman, D. Dobkin,
D. Jacobs, "A search engine for 3d models," ACM Trans. Graphics, vol.
22, 2003, pp. 83-105.
[7] M. Hilaga, Y. Shinagawa, T. Kohmura, T. Kunii, "Topology matching
for fully automatic similarity estimation of 3d shapes." in Proc. the 28th
annual Conf. Computer graphics and interactive techniques, 2001, pp.
203-212.
[8] H. Sundar, D. Silver, N. Gagvani, S. Dickinson, "Skeleton based shape
matching and retrieval," in Proc. International Conf. Shape Modeling
International, 2003, pp. 130-139.
[9] N.D. Cornea, D. Silver, P. Min, "Curve-skeleton properties, applications
and algorithms," IEEE Trans. Visualization and Computer Graphics, vol.
13, 2007, pp. 530-548.
[10] N.D. Cornea, D. Silver, X. Yuan, R. Balasubramanian, "Computing
hierarchical curve-skeletons of 3d objects," in Proc. the Visual Computer,
vol. 21, 2005, pp. 945-955.
[11] A. Brennecke, T. Isenberg, "3d shape matching using skeleton graphs,"
in Proc. Simulation and Visualization, vol. 13, 2004, pp. 299-310
[12] A. Laurentini, "The visual hull concept for silhouette-based image
understanding," IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 16, 1994, pp. 150-162.
[13] M. Li, M. Magnor, H. Seidel, "Hardware-accelerated visual hull reconstruction
and rendering," in Proc. Graphics Interface, 2003, pp. 65-71.
[14] R. Szeliski, "Rapid octree construction from image sequences," in Proc.
CVGIP: Image Underst., vol. 58, 1993, pp. 23-32.
[15] W. Matusik, C. Buehler, L. McMillan, "Polyhedral visual hulls for
real-time rendering," in Proc. the 12th Eurographics Workshop. Rendering
Technique, 2001, pp. 115-126.
[16] C. Lee, J. Cho, K. Oh, "Hardware-accelerated jaggy-free visual hulls
with silhouette maps," in Proc. the ACM Sym. Virtual Reality Software
and Technology, 2006, pp. 87-90.
[17] C. Everitt, A. Rege, C. Cebenoyan, "Hardware shadow mapping,"
Technical report, NVIDIA.
@article{"International Journal of Information, Control and Computer Sciences:53267", author = "Kwangjin Hong and Chulhan Lee and Keechul Jung and Kyoungsu Oh", title = "Real-time 3D Feature Extraction without Explicit 3D Object Reconstruction", abstract = "For the communication between human and computer
in an interactive computing environment, the gesture recognition is
studied vigorously. Therefore, a lot of studies have proposed efficient
methods about the recognition algorithm using 2D camera captured
images. However, there is a limitation to these methods, such as the
extracted features cannot fully represent the object in real world.
Although many studies used 3D features instead of 2D features for
more accurate gesture recognition, the problem, such as the processing
time to generate 3D objects, is still unsolved in related researches.
Therefore we propose a method to extract the 3D features combined
with the 3D object reconstruction. This method uses the modified
GPU-based visual hull generation algorithm which disables unnecessary
processes, such as the texture calculation to generate three kinds
of 3D projection maps as the 3D feature: a nearest boundary, a farthest
boundary, and a thickness of the object projected on the base-plane. In
the section of experimental results, we present results of proposed
method on eight human postures: T shape, both hands up, right hand
up, left hand up, hands front, stand, sit and bend, and compare the
computational time of the proposed method with that of the previous
methods.", keywords = "Fast 3D Feature Extraction, Gesture Recognition,Computer Vision.", volume = "2", number = "8", pages = "2627-6", }