Abstract
A method is described for scale-invariant segregation of image structure solely on the basis of orientation content. This kind of image decomposition is an unexplored image-processing method that is complementary to the well-explored method of filtering in spatial frequency bands; the latter technique is rotation-invariant, whereas the former technique is scale-invariant. The complementarity of these two approaches is explicit in the fact that orientation and spatial frequency are orthogonal variables in the two-dimensional Fourier plane, and the filters employed in the one method depend only on the radial variable, whereas those employed in the other method depend only on the angular variable. The biological significance of multiscale (spatial frequency selective) image analysis has been well-recognized and often cited, yet orientation selectivity is a far more striking property of neural architecture in cortical visual areas. In the present paper, we begin to explore some coding properties of the scale-invariant orientation variable, paying particular attention to its perceptual significance in texture segmentation and compact image coding. Examples of orientation-coded pictures are presented with data compression to 0.3 bits per pixel.
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Beck, J., Prazdny, K., &Rosenfeld, A. (1983). A theory of textural segmentation. In J. Beck, B. Hope, & A. Rosenfeld (Eds.),Human and machine vision (pp. 1–38). New York: Academic Press.
Blakemoke, C, &Campbell, F. W. (1969). On the existence of neurons in the human visual system selectively sensitive to the orientation and size of retinal images.Journal of Physiology (London),203, 237–260.
Brown, J. L., Jr. (1984). Cauchy and polar-sampling theorems.Journal of the Optical Society of America (A),1, 1054–1056.
Burt, P. J. (1984). The pyramid as a structure for efficient computation. In A. Rosenfeld (Ed.),Multiresolution image processing and analysis (pp. 6–38). Berlin: Springer-Verlag.
Burt, P. J., &Adelson, E. H. (1983). The Laplacian pyramid as a compact image code.IEEE Transactions on Communications,COM-31, 532–540.
Caelli, T. M. (1982). On discriminating visual textures and images.Perception & Psychophysics,31, 149–159.
Caelu, T. M. (1985). Three processing characteristics of visual texture segmentation.Spatial Vision,1, 19–30.
Caelli, T. M., &Huebner, M. (1983). Coding images in the frequency domain: Filter design and energy processing characteristics of the human visual system.IEEE Transactions: Systems, Man, & Cybernetics,SMC-13, 1018–1020.
Caelli, T. M., Huebner, M., &Rentschler, I. (1985). The detection of phase-shifts in two-dimensional images: Part 1.Biological Cybernetics,28, 167–175.
Caelli, T. M., &Julesz, B. (1979). On psychophysical evidence for global feature processing in visual texture discrimination.Journal of the Optical Society of America,69, 675–678.
Capeluni, V. (1986). Two-dimensional digital filters and data compression. In P. W. Hawkes (Ed.),Advances in electronics and electron physics (Vol. 66, pp. 141–199). New York: Academic Press.
Casasent, D. (1977). Optical signal processing. In D. Casasent,Topics in applied physics (Vol. 23, Chap. 8). Berlin: Springer-Verlag.
Enroth-Cugell, C, &Robson, J. (1966). The contrast sensitivity of retinal ganglion cells of the cat.Journal of Physiology (London),341, 279–307.
Ginsburg, A. P. (1971).Psychological correlates of a model of the human visual system. Unpublished master’s thesis, Air Force Institute of Technology, Wright Air Development Center, Dayton, OH.
Harmon, L. D., &Julesz, B. (1973). Masking in visual recognition: Effects of two-dimensional filtered noise.Science,180, 1194–1197.
Hartley, R. L. V. (1928). Transmission of information.Bell Systems Technical Journal, 535–563.
Hsu, Y., Arsenault, H., &April, G. (1982). Rotation-invariant digital pattern recognition using circular harmonic expansion.Applied Optics,21, 22–28.
Hubel, D. H, &Wiesel, T. N. (1974). Sequence regularity and geometry of orientation columns in the monkey striate cortex.Journal of Comparative Neurology,158, 267–293.
Hunt, B. R. (1983). Digital image processing. In P. W. Hawkes (Ed),Advances in electronics and electron physics (Vol. 60, pp. 161–221). New York: Academic Press.
Julesz, B. (1980). Spatial-frequency channels in one-, two-, and three-dimensional vision: Variations on an auditory theme by Békésy. In C S. Harris (Ed.),Visual coding and adaptability (pp. 263–317). Hillsdale, NJ: Erlbaum.
Julesz, B. (1981). Textons, the elements of texture perception, and their interactions.Nature,290, 91–97.
Kuffler, S. W. (1953). Discharge patterns and functional organization of mammalian retina.Journal of Neurophysiology,16, 37–68.
Leger, J. R., &Lee, S. H. (1982). Signal processing using hybrid systems. In H. Stark (Ed.),Applications of optical Fourier transforms (pp. 131–205). New York: Academic Press.
Mark, D. (1982).Vision. San Francisco: W. H. Freeman.
Mussman, H. G., Pirsch, P., &Grallert, H.J. (1985). Advances in picture coding.Proceedings of IEEE,73, 523–548.
Pearson, D. E., &Robson, J. A. (1985). Visual communication at very low data rates.Proceedings of IEEE,73, 795–812.
Rentschler, I., &Huebner, M. (1985). Hidden face perception.Human Neurobiology (Vol. 4). Berlin: Springer-Verlag.
Rosenfeld, A. (1984). Some useful properties of pyramids. In A. Rosenfeld (Ed.),Multiresolution image processing and analysis (pp. 2–6). Berlin: Springer-Verlag.
Rosenfeld, A., &Kak, A. (1976).Digital picture processing. New York: Academic Press.
Shannon, C. E., &Weaver, W. (1949).The mathematical theory of communication. Urbana: University of Illinois Press.
Sperling, G., Landy, M., Cohen, Y., & Pavel, M. (1985). Intelligible encoding of ASL image sequences at extremely low information rates.Computer Vision, Graphics, & Image Processing,31, 335–391.
Stark, H. (1979). Sampling theorems in polar coordinates.Journal of the Optical Society of America,69, 1519–1525.
Watt, R. J., &Morgan, M. J. (1985). A theory of the primitive spatial code in human vision.Vision Research,25, 1661–1674.
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We wish to thank R Brockett and the Harvard Robotics Laboratory for their hospitality and assistance This research was partially supported by University Research initiative Project Grant AFOSR
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Daugman, J.G., Kammen, D.M. Pure orientation filtering: A scale-invariant image-processing tool for perception research and data compression. Behavior Research Methods, Instruments, & Computers 18, 559–564 (1986). https://doi.org/10.3758/BF03201429
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DOI: https://doi.org/10.3758/BF03201429