Abstract
Image processing is a loosely defined term whose meaning varies greatly among diverse fields such as digital signal processing, computer vision, computer graphics, remote sensing, neural networks, etc. Naturally, the image processing techniques have diversified involving optics, statistics, mathematics, psychophysics, neurophysics, etc. This paper examines the state of the art in image processing in a limited context where image processing is viewed strictly as a method for retrieving information about an imaged object.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
K. R. Castleman. Digital Image Processing, Prentice-Hall Signal Processing Series, 1979
J. D. Foley, Avan Dam. Fundamentals of Interactive Computer Fraphics, Addison- Wesley, 1984
C. Elachi. Introduction to the Physics and Techniques of Remote Sensing, John Wiley & Sons Inc. 1987
A. Rosenfeld, A. C. Kak. Digital Picture Processing, vol. 1, Academic Press 1982
L. B. Lucy. An iterative technique for the rectification of observed distributions, The Astronomical Journal’, vol. 79, pp 645–754
S. F. Gull, J. Skilling. Maximum Entropy Method in Image Processing, IEE Proc., vol. 131, pt. F, No. 6, pp 646–659
X. Zhuang, E. Ostevold, R. M. Haralick. The principle of maximum entropy in image recovery, Image Recovery: Theory and Applications, ed. H. stark, pp 157–193, Academic Press, NY
R. D. Overheim, D. L. Wagner. Light and Color, John Wiley & Sons, Inc. 1982
J. M. Brady, computer vision, Artificial Intelligence, Vol 17, 1981
D. H. Ballard, C. M. Brown, computer vision, Prentice-Hall 1982
W. K. Pratt. Digital Image Processing, A Wiley-Interscience Publication, John Wiley & Sons, 1978
B. K. P. Horn, M. J. Brooks. The Variational Approach to Shape from Shading, Computer Vision, Graphics, and Image Processing 33, 209–236, 1986
A. Magralit, A. Rosenfeld. Using Probablistic Domain Knowledge to Reduce the Expected Computational Cost of Template Matching, Computer Vision, Graphics, and Image Processing, 51, 219–234, 1990
D. I. Barnea, H. F. Silverman. A class of Algorithms for Fast Digital Image Registration, IEEE Transactions on Computers, c-21, NO. 2, 179–186, 1972
S/ T. Bernard, W.B. Thompson. Disparity Analysis of Images, IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-2, NO. 4, 333–340, 1980
O. D. Faugeras, S. Maybank. Motion from Point Matches: Multiplicity of Solutions, International Computer Vision, 4 225–246, 1990
A. W. Gruen, E. P. Baltsavias. High-Precision Image Matching for Digital Terrain Model Generation, Photogrammetria, 42, 97–112, 1987
M. Lee, C. H. Anderson. Image Matching using Multi-Resolution Pyramid Method, submitted to International Conference in Pattern Recognition, Sept. 1992
P. J. Burt. Fast Filter Transforms for Image Processing, Computer Graphics and Image Processing, pp 20-51, 1981
S. B. Mallat. A Theory for Multiresolution Signal Decomposition, the Wavelet Representation, IEEE Trans. PAMI, vol. 11, pp 674–693, 1989
E. P. Simoncelli and E. H. Adelson. Submand Transform, Subband Image Coding, J. W. Woods, Ed., (Kluwer, Norwell, MA), pp 143–192 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, M., Anderson, C.H., Weidner, R.J. (1993). State of the Art in Image Processing. In: Rudomin, P., Arbib, M.A., Cervantes-Pérez, F., Romo, R. (eds) Neuroscience: From Neural Networks to Artificial Intelligence. Research Notes in Neural Computing, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-78102-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-78102-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56501-7
Online ISBN: 978-3-642-78102-5
eBook Packages: Springer Book Archive