Learning sparse, overcomplete representations of time-varying natural images

IEEE International Conference on Image Processing

Volumn 1, Issue , 2003, Pages 41-44

  Olshausen, Bruno A ^a,b  

^a Redwood Neuroscience Institute   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASIS FUNCTIONS; IMAGE SEQUENCES; TIME-VARYING IMAGES;

APPROXIMATION THEORY; DIFFERENTIAL EQUATIONS; FUNCTIONS; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; PROBABILITY; TIME VARYING SYSTEMS;

IMAGE PROCESSING;

EID: 0345529041     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41: 3397-3415.
2. Olshausen BA, Field DJ (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607-609.
3. Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37, 3311-3325.
4. Rieke F, Warland D, de Ruyter van Stevenick R, Bialek W (1997) Spikes: Exploring the Neural Code. MIT Press.
5. van Hateren JH, Ruderman DL (1998) Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proc.R.Soc.Lond. B, 265:2315-2320.
6. van Hateren (2002) Natural Stimuli Collection. http://hlab.phys.rug.nl/vidlib/vid_db