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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: © 2014 |Pages: 11
ISBN13: 9781466651258|ISBN10: 1466651253|EISBN13: 9781466651265
DOI: 10.4018/978-1-4666-5125-8.ch010
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MLA

Diederichs, Claas, and Sergej Fatikow. "FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics." Nanotechnology: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 251-261. https://doi.org/10.4018/978-1-4666-5125-8.ch010

APA

Diederichs, C. & Fatikow, S. (2014). FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics. In I. Management Association (Ed.), Nanotechnology: Concepts, Methodologies, Tools, and Applications (pp. 251-261). IGI Global. https://doi.org/10.4018/978-1-4666-5125-8.ch010

Chicago

Diederichs, Claas, and Sergej Fatikow. "FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics." In Nanotechnology: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 251-261. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-5125-8.ch010

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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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