TY - GEN
T1 - Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA
AU - Wang, Maolin
AU - Ng, Ho-Cheung
AU - Chung, Bob M.F.
AU - Varma, B. Sharat Chandra
AU - Jaiswal, Manish Kumar
AU - Tsia, Kevin K.
AU - Shum, Ho Cheung
AU - So, Hayden Kwok-Hay
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/5/15
Y1 - 2017/5/15
N2 - A real-time object detection and classification system using FPGA developed for high-speed asymmetric time-stretched optical microscopy (ATOM) framework is presented. Due to the massive amount of data generated by optical frontend, storing the raw data for offline post-processing is slow and impractical for the targeted single cell analysis applications. The proposed FPGA solution eliminates the need to transfer and persist the entire raw data by processing low-level signals and forming highlevel images in real-time. Objects of interest are detected and segmented from the image stream and a classifier subsequently performs high-level analysis on the segmented images. When compared with existing software-based post-processing workflow, this FPGA-based approach will improve both the number of objects captured per experiment and the overall end-to-end object classification performance. The system also allows co-optimization between optical system, low-level signal processing and image analytic in a unified environment that enables new scientific discoveries previously unachievable. © 2016 IEEE.
AB - A real-time object detection and classification system using FPGA developed for high-speed asymmetric time-stretched optical microscopy (ATOM) framework is presented. Due to the massive amount of data generated by optical frontend, storing the raw data for offline post-processing is slow and impractical for the targeted single cell analysis applications. The proposed FPGA solution eliminates the need to transfer and persist the entire raw data by processing low-level signals and forming highlevel images in real-time. Objects of interest are detected and segmented from the image stream and a classifier subsequently performs high-level analysis on the segmented images. When compared with existing software-based post-processing workflow, this FPGA-based approach will improve both the number of objects captured per experiment and the overall end-to-end object classification performance. The system also allows co-optimization between optical system, low-level signal processing and image analytic in a unified environment that enables new scientific discoveries previously unachievable. © 2016 IEEE.
UR - https://www.scopus.com/pages/publications/85021415330
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85021415330&origin=recordpage
U2 - 10.1109/FPT.2016.7929548
DO - 10.1109/FPT.2016.7929548
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509056026
T3 - Proceedings of the 2016 International Conference on Field-Programmable Technology, FPT 2016
SP - 261
EP - 264
BT - Proceedings of the 2016 International Conference on Field-Programmable Technology, FPT 2016
PB - IEEE
T2 - 15th International Conference on Field-Programmable Technology, FPT 2016
Y2 - 7 December 2016 through 9 December 2016
ER -