Abstract
A new method named STM is described for determining distance of objects and rapid autofocusing of camera systems. STM uses image defocus information and is based on a new Spatial-Domain Convolution/Deconvolution Transform. The method requires only two images taken with different camera parameters such as lens position, focal length, and aperture diameter. Both images can be arbitrarily blurred and neither of them needs to be a focused image. Therefore STM is very fast in comparison with Depth-from-Focus methods which search for the lens position or focal length of best focus. The method involves simple local operations and can be easily implemented in parallel to obtain the depth-map of a scene. STM has been implemented on an actual camera system named SPARCS. Experiments on the performance of STM and their results on real-world planar objects are presented. The results indicate that the accuracy of STM compares well with Depth-from-Focus methods and is useful in practical applications. The utility of the method is demonstrated for rapid autofocusing of electronic cameras.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
M. Born and E. Wolf,Principles of Optics, Pergamon Press, Oxford, Sixth Edition, 1980.
J.D. Gaskill,Linear Systems, Fourier Transforms, and Optics, John Wiley and Sons, New York, 1978.
J.W. Goodman,Introduction to Fourier Optics, McGraw-Hill, Inc., 1968.
P. Grossman, “Depth from Focus,”Pattern Recognition Letters 5, pp. 63–69, Jan. 1987.
B.K.P. Horn, “Focusing,” Artificial Intelligence Memo No. 160, MIT, 1968.
B.K.P. Horn,Robot Vision, McGraw-Hill Book Company, 1986.
J. Ens and P. Lawrence, “A Matrix Based Method for Determining Depth from Focus,”Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1991.
R.A. Jarvis, “A Perspective on Range Finding Techniques for Computer Vision,”IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5, No. 2, pp. 122–139, March 1983.
E. Krotkov, “Focusing,”International Journal of Computer Vision, 1, 223–237, 1987.
S. Lai and C. Fu, “A Generalized Depth Estimation Algorithm with a Single Image,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, NO. 4, April 1992, pp. 405–411.
P. Meer and I. Weiss, “Smoothed Differentiation Filters for Images,”Journal of Visual Communication and Image Representation, 3, 1, 1992.
S.K. Nayar, “Shape from Focus System,”Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign, Illinois, pp. 302–308, June 1992.
A.P. Pentland, “A New Sense for Depth of Field,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, No. 4, pp. 523–531, 1987.
A.P. Pentland, “A Simple Real-time Range Camera,”Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, California, June 1989.
J.F. Schlag, A.C. Sanderson, C.P. Neuman, and F.C. Wimberly, “Implementation of automatic focusing algorithms for a computer vision system with camera control,” CMU-RI-TR-83-14, Robotics Institute, Carnegie-Mellon University, 1983.
W.F. Schreiber,Fundamentals of Electronic Imaging Systems, Springer-Verlag, Section 2.5.2., 1986.
M. Subbarao, “Parallel Depth Recovery by Changing Camera Parameters, ”Second International Conference on Computer Vision, Florida, USA, pp. 149–155, December 1988.
M. Subbarao, “Computational Methods and Electronic Camera Apparatus for Determining Distance of Objects, Rapid Autofocusing, and obtaining improved Focus Images,” U.S. patent application serial number 07/373, 996, June 1989 (pending), 1989a.
M. Subbarao, “Efficient Depth Recovery through Inverse Optics,” Editor: H. Freeman,Machine Vision for Inspection and Measurement, Academic Press, Boston, pp. 101–126, 1989b.
M. Subbarao, “Determining Distance from Defocused Images of Simple Objects,” Tech. Report No. 89.07.20, Computer Vision Laboratory, Dept. of Electrical Engineering, State University of New York, Stony Brook, NY 11794-2350, 1989c.
M. Subbarao, “On the Depth Information in the Point Spread Function of a Defocused Optical System,” Tech. Report No. 90.02.07, Feb. 1990, Computer Vision Laboratory, Dept. of Electrical Engineering, State University of New York, Stony Brook, NY 11794-2350.
M. Subbarao, “Spatial-Domain Convolution/Deconvolution Transform,” Tech. Report No. 91.07.03, Computer Vision Laboratory, Dept. of Electrical Engineering, State University of New York, Stony Brook, NY 11794-2350, 1991.
M. Subbarao, T.S. Choi, and A. Nikzad, “Focusing Techniques,” Vol. 1823, Proceedings of SPIE conference, OE/TECHNOLOGY'92, Boston, Nov. 1992, pp. 163–174.
M. Subbarao and M. Lu, “Computer Modeling and Simulation of Camera Defocus,” Vol. 1822,Proceedings of SPIE conference, OE/TECHNOLOGY '92, Boston, Nov. 1992, pp. 110–120.
M. Subbarao and G. Natarajan, “Depth Recovery from Blurred Edges,”Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Ann Arbor, Michigan, pp. 498–503, June 1988.
M. Subbarao and A. Nikzad, “A Model for Image Sensing and Digitization in Machine Vision,” OE/BOSTON90, SPIE conference, Boston, Nov. 1990, Vol. 1385, pp. 70–84.
M. Subbarao and G. Surya, “Application of Spatial-Domain Convolution/Deconvolution Transform for Determining Distance from Image Defocus,” Vol. 1822, Proceedings of SPIE conference, OE/TECHNOLOGY'92, Boston, Nov. 1992, pp. 159–167.
M. Subbarao and T. Wei, “Depth from Defocus and Rapid Autofocusing : A Practical Approach,”Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign, Illinois, June 1992.
J.M. Tenenbaum,Accommodation in Computer Vision, Ph.D. Dissertation, Stanford University, Nov. 1970.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Subbarao, M., Surya, G. Depth from defocus: A spatial domain approach. Int J Comput Vision 13, 271–294 (1994). https://doi.org/10.1007/BF02028349
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02028349