Abstract
Advanced techniques in image processing and computer vision increasingly require that image data be represented at multiple resolutions and at multiple sample rates. Application areas for such pyramid image representations include image compression, image enhancement, motion analysis, and object recognition.
We have developed a VLSI chip, called PYR, to perform the standard filter and resampling operations required in pyramid and inverse pyramid transforms for these applications. The PYR chip processes image samples sequentially, in raster scan format, so is suited for pipeline architectures. The user can choose from a set of standard filters, through software control, to construct Gaussian, Laplacian, Subband, and related pyramid structures.
A unique feature of the design is that it includes timing signals that are passed with the image data. These signals coordinate successive processing steps in a pipeline system as image sizes and sample rates change. The chip also includes circuits for edge extension and image addition, and it can be run in “spread tap” mode to provide twice the standard sample density.
The PYR chip is implemented in standard cell technology. At a clock rate of 15 MHz, a single chip can simultaneously construct a Gaussian and a Laplacian pyramid from a 512 by 480 image in 22.7 msec (44 frame/second).
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Adelson, E.H., Simoncelli, E., and Hingorani, R. 1987. Orthogonal pyramid transforms for image coding, Proc. SPIE Conf. on Visual Communication and Image Processing II, Cambridge, England.
Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion, Intern. J. Comput. Vis. 2: 283–310.
Anderson, C.H. 1984. An alternative to the Burt pyramid algorithm, RCA correspondence.
Anderson, C.H., Burt, P.J., and van der Wal, G.S. 1985. Change detection and tracking using pyramid transform techniques, Proc. SPIE Conf. on Intelligent Robotics and Computer Vision, vol. 579.
Bergen, J.R., and Adelson, E.H. 1987. Hierarchical, computationally efficient motion estimation algorithm, J. Opt. Soc. Am. A4: 35.
Burt, P.J. 1981. Fast filter transforms for image processing, Comput. Graphics Image Process. 16: 20–51.
Burt, P.J. 1988a. Smart sensing within a pyramid vision machine, IEEE Proc. 76 (8).
Burt, P.J. 1988b. Moment images, polynomial fit filters, and the problem of surface interpolation, Proc. Comput. Vis. Patt. Recog., Ann Arbor.
Burt, P.J. 1992. A gradient pyramid basis for pattern selective image fusion, Proc. Soc. Inform. Display Conf.
Burt, P.J., and Adelson, E.H. 1983a. The Laplacian pyramid as a compact image code, IEEE Trans. on Commun. 31 (4).
Burt, P.J., and Adelson, E.H. 1983b. A multiresolution spline with applications to image mosaics, ACM Trans. Commun. 2: 217–236.
Burt, P.J., and Lee, W.A. 1988. A family of pyramid structures for multiresolution image processing, Sarnoff correspondence.
Burt, P.J., and van der Wal, G.S. 1987. Iconic image analysis with the Pyramid Vision Machine (PVM), Proc. Workshop on Computer Architectures for Pattern Analysis and Machine Intelligence, Seattle.
Burt, P.J., and van der Wal, G.S. 1990. An architecture for multiresolution, focal, image analysis, Proc. 10th ICPR, Atlantic City, pp. 305–311.
Cantoni, V., et al. 1985. A pyramid project using integrated technology. In Integrated Technology for Parallel Processing, S. Levialdi, ed., Academic Press, pp. 121–132.
Chehikian, A., and Crowley, J.L. 1991. Fast computation of optimal semi-octave pyramids, 7th Scandinavian Conf. on Image Analysis, Denmark.
Crowley, J.L., and Stern, R.M., 1984. Fast computations for the difference of low-pass transform, IEEE Trans. Patt. Anal. Mach. Intell. 6: 212–221.
Le Gall, D., and Tabatabai, A. 1988. Subband coding of digital images using short kernel filters and arithmetic coding techniques, Proc. Intern. Conf. Acoust., Spch., Sig. Process., New York.
Lucas, B.D., and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision, Proc. Image Understanding Workshop, pp. 121–130.
Mallat, S.G. 1989. A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Patt. Anal. Mach. Intell. 11 (7): 674–693.
Matthies, L. 1991. Stereo vision for planetary rovers, stochastic modeling to near real-time implementation, Jet Propulsion Laboratory report, January.
Merigot, A., et al. 1986. A pyramid system for image processing. In Pyramidal Systems for Computer Vision. V., Cantoni and S., Levialdi, eds. Springer-Verlag: New York.
Quam, L. 1987. Hierarchical warp stereo. In Readings in Computer Vision, M.A., Fischler and O., Firschein, eds., Morgan Kaufmann: Los Altos, CA, pp. 80–86.
Rosenfeld, A. ed., 1984. Multiresolution Image Processing and Analysis. Springer-Verlag: New York.
Tanimoto, S.L. 1984. A hierarchical cellular logic for pyramid computers. J. of Parallel and Distributed Computing 1: 105–132.
Toet, A. 1990. Hierarchical Image Fusion. Mach. Vis. Appl 3: 1–11.
van der Wal, G.S., and Sinniger, J.O. 1985. Real-time pyramid transform architecture. In SPIE Proc. Intelligent Robots and Computer Vision, Boston, pp. 300–305.
Watson, A.B. 1987. The cortex transform: rapid computation of simulated neural images. Comput. Graph. Image Process. 39.
Author information
Authors and Affiliations
Additional information
Division of Applied Sciences Harvard University
Rights and permissions
About this article
Cite this article
Van der Wal, G.S., Burt, P.J. A VLSI pyramid chip for multiresolution image analysis. Int J Comput Vision 8, 177–189 (1992). https://doi.org/10.1007/BF00055150
Issue Date:
DOI: https://doi.org/10.1007/BF00055150