Abstract
Model-based object recognition is typically addressed by first deriving structure from images, and then matching that structure with stored objects. While recognition should be facilitated through the derivar tion of as much structure as possible, most researchers have found that a compromise is necessary, as the processes for deriving that structure are not sufficiently robust. We present a technique for the extraction, and subsequent recognition, of 3-D object models from passively sensed images. Model extraction is performed using a depth from camera motion technique, followed by simple interpolation between the determined depth values. The resultant models are recognised using a new technique, implicit model matching, which was originally developed for use with models derived from actively sensed range data [1]. The technique performs object recognition using secondary representations of the 3-D models, hence overcoming the problems frequently associated with deriving stable model primitives. This paper, then, describes a technique for deriving 3-D structure from passively sensed images, introduces a new approach to object recognition, tests the approach robustness of the approach, and hence demonstrates the potential for object recognition using 3-D structure derived from passively sensed data.
The research described in this paper has been supported by ESPRIT P419, EOLAS APT-VISION and ESPRIT P5363.
This article was processed using the LATEX macro package with ECCV92 style.
Chapter PDF
References
Dawson, K. Vernon, D.: Model-Based 3-D Object Recognition Using Scalar Transform Descriptors. Proceedings of the conference on Model-Based Vision Development and Tools, Vol. 1609, SPIE — The International Society for Optical Engineering (November 1991)
Sandini, G., Tistarelli, M.: Active Tracking Strategy for Monocular Depth Inference Over Multiple Frames. IEEE PAMI, Vol.12, No.1 (January 1980) 13–27
Vernon, D., Tistarelli, M.: Using Camera Motion to Estimate Range for Robotic Part Manipulation. IEEE Robotics and Automation, Vol.6, No.5 (October 1990) 509–521
Vernon, D., Sandini, G. (editors): Parallel computer vision — The VIS a VIS System. Ellis Horwood (to appear)
Horn, B., Schunck, B.: Determining Optical Flow. Artificial Intelligence, Vol.17, No.1 (1981) 185–204
Grimson, W.: From Image to Surfaces: A Computational Study of the Human Early Visual System. MIT Press, Cambridge, Massachusetts (1981)
Faugeras, O., Herbert, M.: The representation, recognition and locating of 3-D objects. International Journal of Robotics Research, Vol. 5, No. 3 (Fall 1986) 27–52
Horn, B.: Extended Gaussian Images. Proceedings of the IEEE, Vol.72, No.12 (December 1984) 1671–1686
Brou, P.: Using the Gaussian Image to Find Orientation of Objects. The International Journal of Robotics Research, Vol.3, No.4 (Winter 1984) 89–125
Dawson, K.: Three-Dimensional Object Recognition through Implicit Model Matching. Ph.D. thesis, Dept. of Computer Science, Trinity College, Dublin 2, Ireland (1991)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dawson, K.M., Vernon, D. (1992). 3-D object recognition using passively sensed range data. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_92
Download citation
DOI: https://doi.org/10.1007/3-540-55426-2_92
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55426-4
Online ISBN: 978-3-540-47069-4
eBook Packages: Springer Book Archive