Knowledge Extraction for High-Throughput Biological Imaging
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.
multilayered architecture, spatiotemperal, biological imaging, segmentation, tracking, object recognition, finite state machine, Petri net based models
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