A Model of Human Feature Detection Based on Matched Filters
Conference paper
Abstract
It is generally accepted that edge and line detection is an important stage of any visual system, biological or artificial. Many algorithms have been developed, either to simulate how humans may detect lines and edges, or as a stage in artificial image processing (see Hildreth, 1985) . Most algorithms convolve the input image with operators of limited bandwidth, and search either for zero-crossings or peaks in the output.
Keywords
Spatial Frequency Local Energy Human Visual System Dark Line Linear Stage
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download
to read the full conference paper text
References
- Adelson, E.H. & Bergen, J.R. (1985) Spatio-temporal energy models for the perception of motion J. Opt. Soc. Am. A2 284–299.CrossRefGoogle Scholar
- Adelson, E.H. & Simoncelli, E. (19 87) Orthogonal pyramid transforms for image coding. Proc SPIE 845 50–58.CrossRefGoogle Scholar
- Anderson, S.J. & Burr, D.C. (1985) Spatial and temporal selectivity of the human motion detection system Vision Res. 25 1147–115.CrossRefGoogle Scholar
- Anderson, S.J. & Burr, D.C. (1987) Receptive field sizes of human motion detectors Vision Res. 27 621–635.CrossRefGoogle Scholar
- Barlow, H.B. (1959) Possible principles underlying the transformations of sensory messages. In Sensory communication (Edited by W.A. Rosenblith). MIT Press, Mass, USA.Google Scholar
- Blakemore, C. & Campbell, F.W. (1969) On the existence of neurones in the visual system selectively sensitive to the orientation and size of retinal images. J. Physiol. (Lond.) 225 437–455.CrossRefGoogle Scholar
- Burr, D.C. (1987) Implications of the Craik-O’Brien illusion for brightness perception. Vision Res. 27 1903–1913.CrossRefGoogle Scholar
- Burr, D.C. & Morrone, M.C. (1987) Inhibitory interactions in the human visual system revealed in pattern visual evoked potentials. J. Physiol. (Lond.) 389 1–21.CrossRefGoogle Scholar
- Burr, D.C. & Morrone, M.C. (1990) Edge detection in biological and artificial visual systems. In Vision: Coding and Efficiency (Edited by C. Blakemore). CUP, Cambridge.Google Scholar
- Burr, D.C, Morrone, M.C. & Spinelli, D. (1989) Evidence for edge and bar detectors in human vision. Vision Res. 29 419–431.CrossRefGoogle Scholar
- Burt, P.J. & Adelson, E.H. (1983) The laplacian pyramid as a compact image code. IEEE Trans COM 31 532–540.CrossRefGoogle Scholar
- Canny, J.F. (1983) Finding edges and lines in images. MIT AI Lab. Tech. Report 720.Google Scholar
- Field, D.J. & Nachmias, J. (1984) Phase reversal discrimination. Vision Res. 24 333–340.CrossRefGoogle Scholar
- Harmon, L.D. & Julesz, B. (1973) Masking in visual recognition: effect of two-dimensional filtered noise. Science 180 1194–1197.CrossRefGoogle Scholar
- Hildreth, E.C. (1985) Edge detection. MIT AI Lab memo. 835.Google Scholar
- Hubel, D.H. & Wiesel, T.N. (1977) Architecture of macaque monkey visual cortex. Proc. Roy. Soc. Lond. B198 1–59.CrossRefGoogle Scholar
- Klein, S.A. & Tyler, C.W. (1981) Phase discrimination of single and compound gratings. Invest. ophthal. Vis. Sci. S20 124.Google Scholar
- Kulikowski, J. J. & Bishop, P. O. (1981) Linear analysis of the responses of simple cells in the cat visual cortex. EXP. Brain Res. 44 386–400.Google Scholar
- Mach, E. (1865) Uber die Wirkung der raumlichen Vertheilung des Lichreizes auf di Neztzhaut. I.S.-B. Akad, Wiss. Wien, math 54 303–322.Google Scholar
- Maffei, L. & Fiorentini, A. (1973) The visual cortex as a spatial frequency analyzer. Vision Res. 13 1255–1267.CrossRefGoogle Scholar
- Marr, D. (1976) Early processing of visual information. Phil Trans. R. Soc. Lond. B275 485–526.Google Scholar
- Marr, D. (1982) Vision Freeman, San Fransisco.Google Scholar
- Marr, D. & Hildreth, E. (1980) Theory of edge detection. Proc. R. Soc. Lond. B207 187–217.CrossRefGoogle Scholar
- Morrone, M.C. & Burr, D.C. (1986) Evidence for the existence and development of visual inhibition in humans. Nature 321 235–237.CrossRefGoogle Scholar
- Morrone, M.C. & Burr, D.C. (1988) Feature detection in human vision: a phase dependent energy model. Proc. R. Soc. (Lond) B235 221–245.CrossRefGoogle Scholar
- Morrone, M.C., Burr, D.C. & Maffei, L. (1982) Functional significance of cross-orientational inhibition: part I Neurophysiology Proc. Rov. Soc. (London) B216 335–354.CrossRefGoogle Scholar
- Morrone, M.C., Burr, D.C. & Ross, J. (1983) Added noise restores recognition of coarse quantised images. Nature 305 226–228.CrossRefGoogle Scholar
- Morrone, M.C., Ross, J., Burr, D.C. & Owens, R. (1986) Mach bands depend on spatial phase. Nature 250–253.Google Scholar
- Morrone, M.C. & Owens, R. (1987) Feature detection from local energy. Pattern Ree. Letters 1 103–113.Google Scholar
- Openheim, A.V. & Lim, J.S. (1981) The importance of phase in signals. Proc. IEEE 69 529–541.CrossRefGoogle Scholar
- Pollen, D.A. & Ronner, S.F. (1981) Phase relationships between adjacent simple cells in the visual cortex. Science 212 1409–141.CrossRefGoogle Scholar
- Ross, J., Morrone, M.C. & Burr, D.C. (1989) The conditions for the appearance of Mach bands. Vision Res. 29 699–715.CrossRefGoogle Scholar
- Spitzer, H. & Hochstein, S. (1985) A complex-cell receptive field model. J. Neurophysiol. 53 1266–1286.Google Scholar
- Watt, R.J. & Morgan, M.J. (1985) A theory of the primitive spatial code in human vision. Vision Res. 25 1661–167.CrossRefGoogle Scholar
- Yuille, A.L. & Poggio, T. (1985) Fingerprint theorems for zero-crossings. J. opt. Soc. Am. A2 683–692.CrossRefMathSciNetGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 1993