Department of Pyschology
Department of Computer Science
Program in Neuroscience.
We have proposed a theory of real-time human object recognition that posits that objects and scenes are represented as an arrangement of simple, viewpoint-invariant volumetric primitives, such as bricks, cylinders, wedges, and cones, termed geons. Geon theory has been implemented as a neural network (Hummel & Biederman, 1992) and has undergone extensive assessment in psychophysical experiments (see Biederman, 1995, for an overview).
This project explores the extent to which the similarity of faces, when they can't be distinguished by easy features, can be modeled in terms of the pattern of activation over a lattice of spatial filters (Biederman & Kalocsai, 1997). Whereas viewpoint invariant properties and an explicit arrangement of distinctive parts appears to suffice for object recognition, a similarity space that preserves the metrics implicit in the original spatial filter activations may suffice for face recognition.
The Neuroscience of Object Recognition
We have two collaborative research projects in which we are investigating the tuning of neurons in IT of the macaque to variations in object shape. In collaboration with Dr. Rufin Vogels of the Katholieke Universiteit of Leuven, Belgium, we have been found that there is stronger tuning to variations to differences in geons than metric properties (such as aspect ratio) (Vogels, Biederman, Bar, & Lorincz, 2000). In collaboration with Prof. Benedek Gysrgy and Dr. Gyula Kov?cs of the Department of Physiology at the Albert Szent-Gysrgyi University in Szeged, Hungary, we have been studying the response of IT neurons to differences in the surface appearance of objects as well as the differential response of these cells to contour-deleted line drawings. What brain areas are active during object recognition? We are investigating this question through fMRI and patient studies (Biederman, Gerhardstein, Cooper, & Nelson, 1997). We are currently conducting an extensive investigation of a very high functioning prosopagnosic individual.
A Theory of Perceptual and Cognitive Pleasure
Vogels, R., Biederman, I., Bar, M, & Lorincz, A. (2000). Inferior temporal neurons show greater sensitivity to nonaccidental than metric differences. Journal of Cognitive Neuroscience, In press. Summary of Vogels et al. (2000) Journal of Cognitive Neuroscience.
Biederman, I., & Kalocsai, P. (1997). Neurocomputational bases of object and face recognition. Philosophical Transactions of the Royal Society London: Biological Sciences, 352, 1203-1219.
Bar, M., & Biederman, I. (1998). Subliminal visual priming. Psychological Science, 9, 464-469.
Bar, M., & Biederman, I. (1999). Localizing the cortical region mediating visual awareness of object identitiy. Proceedings of the National Academy of Sciences, 96, 1790-1793.
Fiser, J., & Biederman, I. (2000). Invariance of visual priming to scale, reflection, translation, and hemisphere. Vision Research, submitted.
Biederman, I., Subramaniam, S., Bar, M., Kalocsai, P, & Fiser, J. (1999). Subordinate-Level Object Classification Reexamined. 62, 131-153. Psychological Research, in press.
Biederman, I., & Bar, M. (1999). One-shot viewpoint invariance in matching novel objects. Vision Research, 39, 2885-2889.
Subramaniam, S., Biederman, I., & Madigan, S. A. (2000). Accurate identification but no priming and chance recognition memory for pictures in RSVP sequences. Visual Cognition, 7, 511-535.
Kirkpatrick-Steger, K., Wasserman, E. A., & Biederman, I. (1999). The pigeon's discrimination of shape and location information. Visual Cognition, 7, 417-436.
Biederman, I., Gerhardstein, P.C. , Cooper, E. E., & Nelson, C. A. (1997). High Level Object Recognition Without an Anterior Inferior Temporal Cortex. Neuropsychologia, 35, 271-287.
Biederman, I. (1995). Visual object recognition. In S. F. Kosslyn and D. N. Osherson (Eds.). An Invitation to Cognitive Science, 2nd edition, Volume 2., Visual Cognition. MIT Press. Chapter 4, pp. 121-165.
Kirkpatrick-Steger, K., Wasserman, E. A., & Biederman, I. (1995). Effects of spatial rearrangement of object components on picture recognition in pigeons. Journal of the Experimental Analysis of Behaviror, 65, 465-475.
Biederman, I., & Gerhardstein, P. C. (1995). Viewpoint-dependent mechanisms in visual object recognition: Reply to Tarr and Bčlthoff (1995). Journal of Experimental Psychology: Human Perception and Performance, 21, 1506-1514.
O'Kane, B., Biederman, I., Cooper, E. E., & Nystrom, B. (1995). An account of object identification confusions. Journal of Experimental Psychology: Applied, 3, 21-41.
Wasserman, E. A., Gagliardi, J. L., Cook, B. R., Kirkpatrick-Steger,
K., Astley, S. L., & Biederman, I. (1995). The Pigeon's Recognition
of Drawings of Depth Rotated Stimuli. Journal of Experimental Psychology:
Animal Behavior Processes,22, 205-221.
Fiser, J., Biederman, I., & Cooper, E. E. (1996). To what extent can matching algorithms based on direct outputs of spatial filters account for human shape recognition? Spatial Vision, 10, 237-271.
Hummel, J. E., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99, 480-517.
Biederman, I., & Bar, M. (2000).
Subramaniam, S., Biederman, I., & Cowie, R. I. D. (1995). Priming the naming of impossible familiar objects.
Biederman, I., & Cooper, E. E. (1995). A direct test of the importance of viewpoint-invariant versus viewpoint-dependent features in object recognition.
Subramaniam, S., Biederman, I., & Cooper, E. E. (1995). Perceiving irregular objects.
Lab Maitre de Chef !!!