PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A wavelet-based model for foveal detection of spatial contrast with frequency dependent aperture effect

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main purpose of this study is to build a Computational model based on ModelFest dataset which is able to predict contrast sensitivity while it benefits from simplicity, efficiency and accuracy, which makes it suitable for hardware implementation, practical uses, online tests, real-time processes, an improved Standard Observer and retina prostheses. It encompasses several components, and in particular, frequency dependent aperture effect (FDAE) which is used for the first time on this dataset, which made the model more accurate and closer to reality. Shortcomings of previous models and the necessity of existence of FDAE for more accuracy led us to develop a new model based on Wavelet Transform that gives us the advantage of speed and the capability to process each frequency channels output. Considering our goal for building an efficient model, we introduce a new formula for modeling contrast sensitivity function, which generates fewer errors and better timing performance. Eventually, this new model leads to having as yet lowest RMS error and solving the problem of long execution time of prior models and reduces them by almost a factor of twenty.
Twórcy
  • Department of biomedical engineering, Amirkabir University, No.10, Behesht Alley, Nazak St., Fifth Golestan, Tehran, Iran
  • Department of biomedical engineering, Amirkabir University, No. 424, Hafez Ave., Tehran, Iran
  • Department of biomedical engineering, Amirkabir University, No. 424, Hafez Ave., Tehran, Iran
Bibliografia
  • 1. Baldwin, A.S. and T.S. Meese, Fourth-root summation of contrast over area: No end in sight when spatially inhomogeneous sensitivity is compensated by a witch's hat. Journal of vision, 2015. 15(15): p. 4-4.
  • 2. Baldwin, A.S., T.S. Meese, and D.H. Baker, The attenuation surface for contrast sensitivity has the form of a witch's hat within the central visual field. Journal of vision, 2012. 12(11): p. 23-23.
  • 3. Batchelor, B.G., Machine Vision Handbook. 2012: Springer.
  • 4. Berkley, M.A., F. Kitterle, and D.W. Watkins, Grating visibility as a function of orientation and retinal eccentricity. Vision Research, 1975. 15(2): p. 239-244.
  • 5. Blakemore, C.t. and F. Campbell, On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. The Journal of physiology, 1969. 203(1): p. 237-260.
  • 6. Bradley, C., J. Abrams, and W.S. Geisler, Retina-V1 model of detectability across the visual field. Journal of vision, 2014. 14(12): p. 22-22.
  • 7. Campbell, F.W. and J. Robson, Application of Fourier analysis to the visibility of gratings. The Journal of physiology, 1968. 197(3): p. 551-566.
  • 8. Carney, T., et al. Modelfest: Year one results and plans for future years. in Electronic Imaging. 2000. International Society for Optics and Photonics.
  • 9. Clatworthy, P., et al., Coding of the contrasts in natural images by populations of neurons in primary visual cortex (V1). Vision research, 2003. 43(18): p. 1983-2001.
  • 10. Daugman, J.G., Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A, 1985. 2(7): p. 1160-1169.
  • 11. Gong, M. and M. Pedersen, Spatial pooling for measuring color printing quality attributes. Journal of Visual Communication and Image Representation, 2012. 23(5): p. 685-696.
  • 12. Gonzalez, R.C., Digital image processing. 2009: Pearson Education India.
  • 13. Goris, R.L., et al., A neural population model for visual pattern detection. Psychological review, 2013. 120(3): p. 472.
  • 14. Goris, R.L., E.P. Simoncelli, and J.A. Movshon, Origin and Function of Tuning Diversity in Macaque Visual Cortex. Neuron, 2015. 88(4): p. 819-831.
  • 15. Graham, C. and R. Margaria, Area and the intensity-time relation in the peripheral retina. American Journal of Physiology--Legacy Content, 1935. 113(2): p. 299-305.
  • 16. Graham, N., Visual detection of aperiodic spatial stimuli by probability summation among narrowband channels. Vision research, 1977. 17(5): p. 637-652.
  • 17. Jones, J.P. and L.A. Palmer, An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of neurophysiology, 1987. 58(6): p. 1233-1258.
  • 18. Levin, L.A., et al., Adler's Physiology of the Eye. 2011: Elsevier Health Sciences.
  • 19. Mallat, S., A wavelet tour of signal processing. 1999: Academic press.
  • 20. Mallat, S., A wavelet tour of signal processing: the sparse way. 2008: Academic press.
  • 21. May, K.A. and J.A. Solomon, Connecting psychophysical performance to neuronal response properties I: Discrimination of suprathreshold stimuli. Journal of vision, 2015. 15(6): p. 8-8.
  • 22. McAnany, J.J. and K.R. Alexander, Spatial contrast sensitivity in dynamic and static additive luminance noise. Vision Research, 2010. 50(19): p. 1957-1965.
  • 23. Meese, T.S. and R.J. Summers, Theory and data for area summation of contrast with and without uncertainty: Evidence for a noisy energy model. Journal of vision, 2012. 12(11): p. 9-9.
  • 24. Pelli, D.G. and P. Bex, Measuring contrast sensitivity. Vision Research, 2013. 90: p. 10-14.
  • 25. Pelli, D.G., et al., Feature detection and letter identification. Vision research, 2006. 46(28): p. 4646-4674.
  • 26. Polikar, R., The story of wavelets. Physics and modern topics in mechanical and electrical engineering, 1999: p. 192-197.
  • 27. Robson, J. and N. Graham, Probability summation and regional variation in contrast sensitivity across the visual field. Vision research, 1981. 21(3): p. 409-418.
  • 28. Rovamo, J., R. Franssila, and R. Näsänen, Contrast sensitivity as a function of spatial frequency, viewing distance and eccentricity with and without spatial noise. Vision Research, 1992. 32(4): p. 631-637.
  • 29. Tyler, C.W., Theory of texture discrimination of based on higher-order perturbations in individual texture samples. Vision Research, 2004. 44(18): p. 2179-2186.
  • 30. Tyler, C.W. and C.-C. Chen, Signal detection theory in the 2AFC paradigm: Attention, channel uncertainty and probability summation. Vision research, 2000. 40(22): p. 3121-3144.
  • 31. Valberg, A., Light vision color. 2007: John Wiley & Sons.
  • 32. Vetterli, M. and C. Herley, Wavelets and filter banks: Theory and design. Signal Processing, IEEE Transactions on, 1992. 40(9): p. 2207-2232.
  • 33. Vetterli, M. and J. Kovacevic, Wavelets and subband coding. 1995: Prentice-hall.
  • 34. Walker, L., S. Klein, and T. Carney. Modeling the Modelfest data: decoupling probability summation. in Optical Society ofAmerica Annual Meeting, Digest ofTechnical Papers, pp. SuC5. 1999.
  • 35. Wang, Z. and X. Shang. Spatial Pooling Strategies for Perceptual Image Quality Assessment. in Image Processing, 2006 IEEE International Conference on. 2006.
  • 36. Watson, A., Visual detection of spatial contrast patterns: Evaluation of five simple models. Optics Express, 2000. 6(1): p. 12-33.
  • 37. Watson, A., C.V. Ramirez, and E. Salud, Predicting visibility of aircraft. PloS one, 2009. 4(5): p. e5594.
  • 38. Watson, A.B., 31.1: Invited Paper: The Spatial Standard Observer: A Human Vision Model for Display Inspection. SID Symposium Digest of Technical Papers, 2006. 37(1): p. 1312-1315.
  • 39. Watson, A.B. ModelFest. 1999; Available from: http://vision.arc.nasa.gov/modelfest/.
  • 40. Watson, A.B., Probability summation over time. Vision research, 1979. 19(5): p. 515-522.
  • 41. Watson, A.B., Spatial standard observer. 2012, US Patent 8,139,892.
  • 42. Watson, A.B. and A.J. Ahumada, Letter identification and the Neural Image Classifier. Journal of vision, 2015. 15(2): p. 15-15.
  • 43. Watson, A.B. and J.A.J. Ahumada, A standard model for foveal detection of spatial contrast. Journal of Vision, 2005. 5(9): p. 6-6.
  • 44. Willmore, B., et al., The berkeley wavelet transform: a biologically inspired orthogonal wavelet transform. Neural computation, 2008. 20(6): p. 1537-1564.
  • 45. Yantis, S. and H. Pashler, Stevens’ Handbook of Experimental Psychology: Vol. 1. Sensation and Perception. 2002, New York: Wiley.
  • 46. Zewdie, C.G., M. Pedersen, and W. Zhaohui. A new pooling strategy for image quality metrics: Five number summary. in Visual Information Processing (EUVIP), 2014 5th European Workshop on. 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-de184fe2-b50c-46bf-80ed-def2da39705b
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.