Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Author(s): Carpenter, G.A. | Martens, S. | Mingolla, E. | Ogas, O. J. | Sai, C. |

Year: 2004

Citation: Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop (AIPR 2004), Los Alamitos, CA: IEEE Computer Society, 61-65.

Abstract: Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically combine bottom-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from input labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs ( and are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.

Topics: Biological Vision, Machine Learning, Models: Other,

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