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FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics

FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics

Claas Diederichs, Sergej Fatikow
Copyright: © 2013 |Volume: 3 |Issue: 1 |Pages: 11
ISSN: 2156-1664|EISSN: 2156-1656|EISBN13: 9781466631335|DOI: 10.4018/ijimr.2013010103
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MLA

Diederichs, Claas, and Sergej Fatikow. "FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics." IJIMR vol.3, no.1 2013: pp.27-37. http://doi.org/10.4018/ijimr.2013010103

APA

Diederichs, C. & Fatikow, S. (2013). FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics. International Journal of Intelligent Mechatronics and Robotics (IJIMR), 3(1), 27-37. http://doi.org/10.4018/ijimr.2013010103

Chicago

Diederichs, Claas, and Sergej Fatikow. "FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics," International Journal of Intelligent Mechatronics and Robotics (IJIMR) 3, no.1: 27-37. http://doi.org/10.4018/ijimr.2013010103

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Abstract

Object-detection and classification is a key task in micro- and nanohandling. The microscopic imaging is often the only available sensing technique to detect information about the positions and orientations of objects. FPGA-based image processing is superior to state of the art PC-based image processing in terms of achievable update rate, latency and jitter. A connected component labeling algorithm is presented and analyzed for its high speed object detection and classification feasibility. The features of connected components are discussed and analyzed for their feasibility with a single-pass connected component labeling approach, focused on principal component analysis-based features. It is shown that an FPGA implementation of the algorithm can be used for high-speed tool tracking as well as object classification inside optical microscopes. Furthermore, it is shown that an FPGA implementation of the algorithm can be used to detect and classify carbon-nanotubes (CNTs) during image acquisition in a scanning electron microscope, allowing fast object detection before the whole image is captured.

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