Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-07-01T21:10:59.849Z Has data issue: false hasContentIssue false

Single-Band Amplitude Demodulation of Müller-Lyer Illusion Images

Published online by Cambridge University Press:  10 April 2014

Vicente Sierra-Vázquez*
Affiliation:
Universidad Complutense, Madrid
Ignacio Serrano-Pedraza
Affiliation:
University of Newcastle
*
Address correspondence to: Dr. V. Sierra-Vázquez, Departamento de Psicología Básica I, Facultad de Psicología, Universidad Complutense de Madrid, Campus de Somosaguas, 28223 Madrid, Spain. Phone: +34 913 943 144. Fax: +34 913 943 189. E-mail: vicente@psi.ucm.es

Abstract

The perception of the Müller-Lyer illusion has previously been explained as a result of visual low band-pass spatial filtering, although, in fact, the illusion persists in band-pass and high-pass filtered images without visible low-spatial frequencies. A new theoretical framework suggests that our perceptual experience about the global spatial structure of an image corresponds to the amplitude modulation (AM) component (or its magnitude, also called envelope) of its AM-FM (alternatively, AM-PM) decomposition. Because demodulation is an ill-posed problem with a non-unique solution, two different AM-FM demodulation algorithms were applied here to estimate the envelope of images of Müller-Lyer illusion: the global and exact Daugman and Downing (1995) AMPM algorithm and the local and quasi-invertible Maragos and Bovik (1995) DESA. The images used in our analysis include the classic configuration of illusion in a variety of spatial and spatial frequency content conditions. In all cases, including those of images for which visual low-pass spatial filtering would be ineffective, the envelope estimated by single-band amplitude demodulation has physical distortions in the direction of perceived illusion. It is not plausible that either algorithm could be implemented by the human visual system. It is shown that the proposed second order visual model of pre-attentive segregation of textures (or “back-pocket” model) could recover the image envelope and, thus, explain the perception of this illusion even in Müller-Lyer images lacking low spatial frequencies.

La percepción de la ilusión de Müller-Lyer ha sido explicada como resultado del filtrado visual paso-bajo de las imágenes en las que aparece, aunque, de hecho, la ilusión se percibe en imágenes paso-banda y paso-alto carentes de bajas frecuencias espaciales. Una nueva manera de pensar acerca del procesamiento visual espacial sugiere que la percepción de la estructura espacial global de una imagen se corresponde con el componente de amplitud modulada (AM) o envolvente resultante de su descomposición AM-FM (o, alternativamente, de su descomposición AM-PM). En este trabajo, la envolvente de imágenes de la ilusión de Müller-Lyer se estimó mediante dos algoritmos de demodulación: el algoritmo AMPM de Daugman y Downing (1995) y DESA de Maragos & Bovik (1995). Las imágenes de Müller-Lyer utilizadas presentan la configuración clásica de la ilusión en diferentes versiones espaciales y con diferente contenido en frecuencia espacial. Para cada una de las imágenes utilizadas, incluidas aquellas en las que su filtrado paso-bajo es inútil para obtener su estructura global, la envolvente estimada mediante la demodulación de la amplitud presenta distorsiones físicas que se corresponden con la ilusión percibida. Es poco plausible que el sistema visual humano implemente cualquiera de los dos algoritmos utilizados. Sin embargo, se muestra que el modelo de mecanismos visuales de segundo orden propuesto para la segregación preatencional de la textura puede recuperar la envolvente de los estímulos visuales, explicándose así la percepción de la ilusión de Müller-Lyer aún en imágenes carentes de bajas frecuencias espaciales.

Type
Articles
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bergen, J.R., & Adelson, E.H. (1988). Early vision and texture perception. Nature, 333, 363364.CrossRefGoogle ScholarPubMed
Bovik, A.C., Clark, M., & Geisler, W.S. (1990). Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 5573.CrossRefGoogle Scholar
Bovik, A.C., Gopal, N., Emmoth, T., & Restrepo Palacios, A. (1992). Localized measurement of emergent image frequencies by Gabor wavelets. IEEE Transactions on Information Theory, 38, 691712.CrossRefGoogle Scholar
Boring, E.G. (1942). Sensation and perception in the history of experimental psychology. New York: Appleton-Century-Crofts.Google Scholar
Bracewell, R.N. (1978). The Fourier transform and its applications (2nd ed.).New York: McGraw-Hill.Google Scholar
Brentano, F. (1892). Über ein optisches Paradoxen. Zeitschrift für Psychologie, 3, 349358.Google Scholar
Bülow, T., & Sommer, G. (1999). A novel approach to the 2D analytic signal. In Solina, F. & Leonardis, A. (Eds.), 8th Conference on Computer Analysis of Images and Patterns, Ljubljana (Vol. 1689 of LNCS, pp. 2532). Berlin: Springer-Verlag.CrossRefGoogle Scholar
Carlson, C.R., Moeller, J.R., & Anderson, C.H. (1984). Visual illusions without low spatial frequencies. Vision Research, 24, 14071413.CrossRefGoogle ScholarPubMed
Carrasco, M., Figueroa, J.G., & Willen, J.D (1986). A test of the spatial-frequency explanation of the Müller-Lyer illusion. Perception, 15, 553562.CrossRefGoogle ScholarPubMed
Coren, S., & Girgus, J.S. (1978). Visual illusions. In Held, R., Leibowitz, H.W., & Teuber, H.-L. (Eds.), Handbook of sensory physiology. Vol VIII. Perception (pp. 551568). Berlin: Springer-Verlag.Google Scholar
Chubb, C., & Landy, M.S. (1991). Orthogonal distribution analysis: A new approach to the study of texture perception. In Landy, M.S. & Movshon, J.A. (Eds.), Computational models of visual processing (pp. 291301). Cambridge, MA: MIT Press.Google Scholar
Daugman, J.G., & Downing, C.J. (1993). Demodulation: A new approach to texture vision. In Harris, L.H. & Jenkin, M. (Eds.), Spatial vision in human and robots (pp. 6387). Cambridge, UK. Cambridge University Press.Google Scholar
Daugman, J.G., & Downing, C.J. (1995). Demodulation, predictive coding, and spatial vision. Journal of the Optical Society of America A, 4, 641660.CrossRefGoogle Scholar
Felsberg, M., & Sommer, G. (2001). The monogenic signal. IEEE Transactions on Signal Processing, 49, 31363144.CrossRefGoogle Scholar
García-Pérez, M.A. (1991). Visual phenomena without low spatial frequencies: A closer look. Vision Research, 31, 16471653.CrossRefGoogle ScholarPubMed
Ginsburg, A.P. (1978). Visual information processing based on spatial filters constrained by biological data. Technical Report AMRL-TR-78-129. Vol I & II. Aerospace Medical Research Laboratory. Dayton, OH: Wright Patterson Air Force Base.Google Scholar
Ginsburg, A.P. (1986). Spatial filtering and visual form perception. In Boff, K., Kauffman, L., & Thomas, J.P. (Eds.), Handbook of perception and human performance. Vol II. Cognitive processes and performance (pp. 34.134.41). New York: Wiley.Google Scholar
Goldstein, E.B. (1984). Sensation and perception, (2nd ed.). Belmont, CA: Wadsworth.Google Scholar
Gregory, R. (1990). Eye and brain. The psychology of seeing (4th ed.). Oxford, UK: Oxford University Press.Google Scholar
Hartmann, W.M. (1998). Signals, sound, and sensation. New York: Springer-Verlag.CrossRefGoogle Scholar
Havlicek, J.P., & Bovik, A.C. (2000). Image modulation models.. In Bovick, A.C. (Ed.), Handbook of image and video processing (pp. 313324). New York: Academic Press.Google Scholar
Heymans, G. (1896). Quantitative Untersuchungen über das “optische Paradoxon”. Zeitschrift für Psychologie, 9, 221255.Google Scholar
Kaiser, J.F. (1990). On a simple algorithm to calculate the energy of a signal. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, April, 381384.Google Scholar
Landy, M. S., & Oruç, I. (2002). Properties of second-order spatial frequency channels. Vision Research, 42, 23112329.CrossRefGoogle ScholarPubMed
Loughlin, P.J., & Tacer, B. (1996). On the amplitude- and frequency-modulation decomposition of signals. Journal of Acoustical Society of America, 100, 15941601.CrossRefGoogle Scholar
Malik, J., & Perona, P. (1990). Preattentive texture discrimination with early vision mechanisms. Journal of the Optical Society of America A, 7, 923932.CrossRefGoogle ScholarPubMed
Maragos, P., & Bovik, A.C. (1995). Image demodulation using multidimensional energy separation. Journal of the Optical Society of America A, 12, 18671876.CrossRefGoogle Scholar
Maragos, P., Kaiser, J.F., & Quatieri, T.F. (1993). On amplitude and frequency demodulation using energy operators. IEEE Transactions on Signal Processing, 41, 15321550.CrossRefGoogle Scholar
Morrone, M.C., & Burr, D.C. (1988). Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society, London, B, 235, 221245.Google Scholar
Müller-Lyer, F.C. (1889). Optische Urtheilstäuschungen. Archiv für Anatomie und Physiologie, Physiologische Abteilung 2 (Suppl.), 263270.Google Scholar
Müller-Lyer, F.C. (1896). Zur Lehre von den optischen Täuschungen. Über Kontrast und Konfluxion. Zeitschrift für Psychologie, 9, 116.Google Scholar
NAG (1991). NAG Fortran Library manual-Mark 15. Oxford: Numerical Algorithm Group.Google Scholar
Oppenheim, A.V., & Lim, J.S. (1981). The importance of phase in signals. Proceedings of the IEEE, 69, 529541.CrossRefGoogle Scholar
Papoulis, A. (1962). The Fourier integral and its applications. New York: McGraw-Hill.Google Scholar
Peli, E. (1992). Perception and interpretation of high-pass filtered images. Optical Engineering, 31, 7481.CrossRefGoogle Scholar
Piotrowski, L.N., & Campbell, F.W. (1982). A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception, 11, 337356.CrossRefGoogle ScholarPubMed
Robinson, J.O. (1972). The psychology of visual illusion. London: Hutchinson University Library.Google Scholar
Schofield, A.J. (2000). What does second order-vision see in an image? Perception, 29, 10711086.CrossRefGoogle ScholarPubMed
Schofield, A.J., & Georgeson, M.A. (2003). Sensitivity to contrast modulation: The spatial frequency dependence of second-order vision. Vision Research, 43, 243259.CrossRefGoogle ScholarPubMed
Sekuler, R., & Blake, R. (1994). Perception (3rd ed). New York: McGraw-Hill.Google Scholar
Skottun, B.C. (2000). Amplitude and phase in the Müller-Lyer illusion. Perception, 29, 201209.CrossRefGoogle ScholarPubMed
Sierra-Vázquez, V., & García-Pérez, M.A. (1995). Psychophysical 1-D wavelet analysis and the appearance of visual contrast illusions. IEEE Transactions on Systems, Man, and Cybernetics, 25, 14241433.CrossRefGoogle Scholar
Sierra-Vázquez, V., & Serrano-Pedraza, I. (2003, August). Anisotropic amplitude demodulation of Müller-Lyer illusion. 34th European Mathematical Psychology Group Meeting (abstract). MadridGoogle Scholar
Sierra-Vázquez, V., & Serrano-Pedraza, I. (2004). Riesz transforms for the isotropic envelope estimation of Müller-Lyer illusion images. Perception (Suppl.), 33, 81 (abstract).Google Scholar
Sierra-Vázquez, V., & Serrano-Pedraza, I. (2006, August). Application of Riesz transforms to AM-PM decomposition of geometrical optical illusions images. International Congress of Mathematicians, Madrid. Abstracts, 178.Google Scholar
Smith, Z.M., Delgutte, B., & Oxenham, A.J. (2002). Chimaeric sounds reveal dichotomies in auditory perception. Nature, 416, 8790.CrossRefGoogle ScholarPubMed
Tadmor, Y., & Tolhurst, D.J. (1993). Both the phase and the amplitude spectrum may determine the appearance of natural images. Vision Research, 33, 141145.CrossRefGoogle ScholarPubMed
Vakman, D.E. (1972). On the definition of concepts of amplitude, phase, and instantaneous frequency of a signal. Radio Engineering and Electronics Physics, 17, 754759.Google Scholar