Single-frame super-resolution by a cortex based mechanism using high level visual features in natural images

Proceedings of IEEE Workshop on Applications of Computer Vision

Volumn 2002-January, Issue , 2002, Pages 112-117

  Kursun O ^a   Favorov, O ^a  

^a University of Central Florida   (United States)

Author keywords

Biological system modeling; Computer science; Environmental factors; Image edge detection; Image generation; Image resolution; Interpolation; Pixel; Polynomials; Spatial resolution

Indexed keywords

BIOLOGICAL SYSTEMS; COMPUTER SCIENCE; COMPUTER VISION; EDGE DETECTION; IMAGE RESOLUTION; INTERPOLATION; OPTICAL RESOLVING POWER; PIXELS; POLYNOMIALS;

BIOLOGICAL SYSTEM MODELING; ENVIRONMENTAL FACTORS; IMAGE EDGE DETECTION; IMAGE GENERATIONS; SPATIAL RESOLUTION;

IMAGE TEXTURE;

EID: 84948735385     PISSN: 21583978     EISSN: 21583986     Source Type: Conference Proceeding    
DOI: 10.1109/ACV.2002.1182167     Document Type: Conference Paper

References (14)

1. Attick JJ, Redlich AN (1992) What does the retina know about natural scenes. Neural Computation 4: 196-210
2. Baker S, Kanade T (2001) Super-resolution: reconstruction or recognition? IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, IEEE, Baltimore, Maryland
3. Becker S, Hinton GE (1992) A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature 355: 161-163
4. Clark A, Thornton C (1997) Trading places: computation, representation, and the limits of uninformed learning. Behavioral and Brain Sciences 20: 57-90
5. Favorov OV, Ryder D (2002) SINBAD: A neocortical mechanism for discovering environmental variables and regularities hidden in sensory input. Biological Cybernetics (submitted)
6. Field DJ (1994) What is the goal of sensory coding. Neural Computation 6:559-601
7. Foldiak P (1990) Forming sparse representations by local anti-Hebbian learning. Biological Cybernetics 64: 165-170
8. Freeman WT, Jones TR, Pasztor EC (2001) Example-Based Super-Resolution. Mitsubishi Electronic Research Laboratory, TR-2001-30
9. Keys R (1981) Bicubic interpolation. IEEE Trans. Acoust. Speech, Signal Processing 29:1153-1160
10. Mumford D (1991) On the computational architecture of the neocortex. Biological Cybernetics 65: 135-145
11. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607-609
12. Phillips WA, Singer W (1997) In search of common foundations for cortical computation. Behavioral and Brain Sciences 20: 657-722
13. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition, MIT Press, Cambridge, Mass, 1: 318-362
14. Ryder D, Favorov OV (2001) The new associationism: a neural explanation for the predictive powers of cerebral cortex. Brain and Mind 2: 161-194