Face Recognition with Quantum Associative Networks Using Overcomplete Gabor Wavelet

Cognitive Computation

Volumn 2, Issue 4, 2010, Pages 297-302

  Tay, Nuo Wi ^a   Loo, Chu Kiong ^a   Peruš, Mitja ^b  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Face recognition; Gabor wavelet; Information maximization; Invariance; Quantum Holography

Indexed keywords

BASIS FUNCTIONS; GABOR WAVELETS; INFOMAX; INFORMATION MAXIMIZATION; MATHEMATICAL DESCRIPTOR; NATURAL SCENES; OVER-COMPLETE; OVERCOMPLETE REPRESENTATIONS; QUANTUM ASSOCIATIVE NETWORKS; QUANTUM HOLOGRAPHY; RECEPTIVE FIELDS;

HOLOGRAPHY; OPTIMIZATION;

FACE RECOGNITION;

EID: 78649913777     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-010-9047-2     Document Type: Article

References (23)

1. Bastiaans M. Gabor's expansion of a signal into gaussian elementary signals. Proc IEEE. 1980; 68: 538-9.
2. Bell A, Sejnowski T. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995; 7: 1129-59.
3. Bell A, Sejnowski T. The "independent components" of natural scenes are edge filters. Vision Res. 1997; 37: 3327-38.
4. Buser P, Imbert M. Vision. Cambridge, MA: MIT Press; 1992.
5. Daugman J. Complete discrete 2-D gabor transforms by neural networks for image analysis and compression. IEEE Trans Acoust. 1988; 36: 1169-79.
6. Field DJ. Scale-invariance and self-similar 'wavelet' transforms: an analysis of natural scenes and mammalian visual systems. In: Wavelets, fractals and fourier transforms: new development and new applications. Oxford: Oxford University Press; 1993.
7. Grossmann A, Kronland-Martinet R, Morlet J. Reading and understanding continuous wavelet transforms. In: Combes JM, Grossman A, Tchamitchian P, editors. Wavelets. Berlin: Springer; 1989. p. 2-20.
8. Hubel D, Wiesel T. Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex. J Physiol (Lond). 1962; 160: 106-54.
9. Jones J, Palmer L. An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol. 1987; 58: 1233-58.
10. Kyrki V, Kamarainen JK. Kälviäinen.: simple Gabor feature space for invariant object recognition. Pattern Recognit Lett. 2004; 25: 311-8.
11. Lee T. Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell. 1996; 18(10): 1-13.
12. Linsker R. Self-organization in a perceptual network. Computer. 1988; 21(3): 105-17.
13. Marcelja S. Mathematical description of the responses of simple cortical cells. J Opt Soc Am. 1980; 70: 1297-300.
14. Okajima K. The Gabor function extracts the maximum information from input local signals. Neural Netw. 1998; 11: 435-9.
15. Olshausen B, Field D. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nat Lett. 1996; 381: 607-9.
16. Olshausen B, Field D. Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Res. 1997; 37: 3311-25.
17. Palmer S. Vision science (photons to phenomenology). Cambridge, MA: MIT Press; 1999.
18. Peruš M. A synthesis of the Pribram Holonomic theory of vision with quantum associative nets after pre-processing using ICA and other computational models. Int J Comput Anticipatory Syst. 2001; 10: 352-67.
19. Peruš M. Multi-level synergetic computation in brain. Non-linear phenom. Complex Syst. 2001; 4: 157-93.
20. Peruš M, Bischof H, Caulfield HJ, Loo CK. Quantum-implementable selective reconstruction of high-resolution images. Appl Opt. 2004; 43(33): 6134-8.
21. Peruš M, Bischof H, Loo CK. Bio-computational model of object-recognition: quantum Hebbian processing with neurally shaped Gabor wavelets. Biosystems. 2005; 82: 116-26.
22. Pollen D, Ronner S. Phase relationships between adjacent simple cells in the visual cortex. Science. 1981; 212: 1409-11.
23. Pribram KH. Brain and perception (holonomy and structure in figural processing). Hillsdale, NJ: Lawrence Erlbaum Associates; 1991.