Title
Knowledge Extraction for High-Throughput Biological Imaging
Abstract
We present a multilayered architecture and spatiotemporal models for searching, retrieving, and analyzing high-throughput biological imaging data. The analysis is divided into low- and high-level processing. At the lower level, we address issues like segmentation, tracking, and object recognition. At the high level, we use finite-state-machine- and Petri-net-based models for spatiotemporal event recognition.
Keywords
multilayered architecture, spatiotemperal, biological imaging, segmentation, tracking, object recognition, finite state machine, Petri net based models
Date of this Version
2007
COinS
Authoritative Citation
MultiMedia, October-December 2007 (vol. 14 no. 4), pages 52-62.