@article{Die2013,Author = {Diederichs, Claas and Fatikow, Sergej},Title = {FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics},Journal = {International Journal of Intelligent Mechatronics and Robotics},Year = {2013},Number = {1},Pages = {27-37},Edition = {3},type = {article},note = {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 super},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.}}@COMMENT{Bibtex file generated on }