Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA

Maolin Wang, Ho-Cheung Ng, Bob M.F Chung, B. Sharat Chandra Varma, Manish Kumar Jaiswal, Kevin K. Tsia, Ho Cheung Shum, Hayden Kwok-Hay So

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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 high-level 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.
Original languageUndefined/Unknown
Title of host publication2016 International Conference on Field-Programmable Technology (FPT): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-264
Number of pages4
ISBN (Electronic)978-1-5090-5602-6
ISBN (Print)978-1-5090-5603-3
DOIs
Publication statusPublished - 18 May 2017

Keywords

  • Atom optics
  • Detectors
  • Field programmable gate arrays
  • Hardware
  • Image segmentation
  • Imaging
  • Real-time systems

Cite this

Wang, M., Ng, H-C., Chung, B. M. F., Chandra Varma, B. S., Jaiswal, M. K., Tsia, K. K., ... So, H. K-H. (2017). Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. In 2016 International Conference on Field-Programmable Technology (FPT): Proceedings (pp. 261-264). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FPT.2016.7929548
Wang, Maolin ; Ng, Ho-Cheung ; Chung, Bob M.F ; Chandra Varma, B. Sharat ; Jaiswal, Manish Kumar ; Tsia, Kevin K. ; Shum, Ho Cheung ; So, Hayden Kwok-Hay. / Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. 2016 International Conference on Field-Programmable Technology (FPT): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 261-264
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title = "Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA",
abstract = "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 high-level 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.",
keywords = "Atom optics, Detectors, Field programmable gate arrays, Hardware, Image segmentation, Imaging, Real-time systems",
author = "Maolin Wang and Ho-Cheung Ng and Chung, {Bob M.F} and {Chandra Varma}, {B. Sharat} and Jaiswal, {Manish Kumar} and Tsia, {Kevin K.} and Shum, {Ho Cheung} and So, {Hayden Kwok-Hay}",
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Wang, M, Ng, H-C, Chung, BMF, Chandra Varma, BS, Jaiswal, MK, Tsia, KK, Shum, HC & So, HK-H 2017, Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. in 2016 International Conference on Field-Programmable Technology (FPT): Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 261-264. https://doi.org/10.1109/FPT.2016.7929548

Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. / Wang, Maolin; Ng, Ho-Cheung; Chung, Bob M.F; Chandra Varma, B. Sharat ; Jaiswal, Manish Kumar; Tsia, Kevin K.; Shum, Ho Cheung; So, Hayden Kwok-Hay.

2016 International Conference on Field-Programmable Technology (FPT): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 261-264.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 - Chandra Varma, B. Sharat

AU - Jaiswal, Manish Kumar

AU - Tsia, Kevin K.

AU - Shum, Ho Cheung

AU - So, Hayden Kwok-Hay

PY - 2017/5/18

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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 high-level 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.

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 high-level 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.

KW - Atom optics

KW - Detectors

KW - Field programmable gate arrays

KW - Hardware

KW - Image segmentation

KW - Imaging

KW - Real-time systems

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DO - 10.1109/FPT.2016.7929548

M3 - Conference contribution

SN - 978-1-5090-5603-3

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EP - 264

BT - 2016 International Conference on Field-Programmable Technology (FPT): Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Wang M, Ng H-C, Chung BMF, Chandra Varma BS, Jaiswal MK, Tsia KK et al. Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. In 2016 International Conference on Field-Programmable Technology (FPT): Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 261-264 https://doi.org/10.1109/FPT.2016.7929548