Current challenges in automating visual perception

Proceedings - Applied Imagery Pattern Recognition Workshop

Volumn , Issue , 2008, Pages

  Oertel, Carsten ^a   Colder, Brian ^b   Colombe, Jeffrey ^a   High, Julia ^a   Ingram, Michael ^c   Sallee, Phil ^d  

^a MITRE CORPORATION   (United States)

^b Colder Scientific ^*

^c ITT   (United States)

^d BOOZ ALLEN HAMILTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

2D IMAGES; ACQUISITION PARAMETERS; COREGISTRATION; NATURALLY OCCURRING; OBJECT APPEARANCE; OBJECT REPRESENTATIONS; UNDERLYING MECHANISM; VISION PROBLEMS; VISUAL PERCEPTION;

COMPUTATIONAL COMPLEXITY; COMPUTER VISION; IMAGE QUALITY; IMAGE SEGMENTATION;

THREE DIMENSIONAL;

EID: 69949109602     PISSN: 15505219     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AIPR.2008.4906457     Document Type: Conference Paper

References (20)

1. Fuller B (1975) Synergetics: Explorations in the Geometry of Thinking. New York: Macmillan.
2. Feldman J (2003) What is a visual object? Trends in Cognitive Sciences 7:252-256.
3. Rosch E, Mervis CB, Gray WD, Johnson DM, and Boyes-Braem P (2005) Basic objects in natural categories. In: Cognitive Psychology, Balota DA and Marsh EJ, eds.
4. Adelson EH (2001) On seeing stuff: The perception of materials by humans and machines. Proceedings of the SPIE Human Vision and Electronic Imaging VI, Rogowitz BE and Pappas TN, eds., 4299:1-12.
5. Mel BW (2000) Minimizing binding errors using learned conjunctive features. Neural Computation 12:731-762.
6. Roweis ST and Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-2326.
7. Tenenbaum JB, de Silva V, and Langford JC (2000) A global framework for nonlinear dimensionality reduction. Science 290:2319-2323.
8. Rao RPN and Sejnowski TJ (1999) Learning Lie groups for invariant visual perception. Advances in Neural Information Processing Systems 11:810-816.
9. Tenenbaum JB and Freeman WT (2000) Separating style and content with bilinear models. Neural Computation 12:1247-1283.
10. Grimes DB (2005) Bilinear sparse coding for invariant vision. Neural Computation 17:47-73.
11. Tappen MF, Adelson EH, and Freeman WT (2006) Estimating intrinsic component images using non-linear regression. Proceedings of 2006 IEEE Conference on Computer Vision and Pattern Recognition pp1992-1999.
12. Purves D and Lotto RB (2003) Why We See What We Do: An Empirical Theory of Vision. Sinauer Associates.
13. Marr D (1983) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. WH Freeman.
14. Tsao DY et al. (2003) Stereopsis activates V3A and caudal intraparietal areas in macaques and humans. Neuron 39:555-568.
15. Lowe DG (1999) Object recognition from local scale-invariant features. International Conference on Computer Vision, Corfu, Greece, Sept 1999. 1150-1157.
16. Lowe DG (2004) Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision 60:91:110.
17. Foster DH and Gilson SJ (2002) Recognizing novel threedimensional objects by summing signals from parts and views. Proceedings of the Royal Society of London B 269:1939-1947.
18. Hastie T, Tibshirani R, and Friedman J (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
19. Sutton RS and Barto AG (1998) Reinforcement Learning: An Introduction. Cambridge: MIT Press.
20. Duda RO, Hart PE, and Stork DG (2001) Pattern Classification, 2nd Ed. New York: John Wiley & Sons.