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.
|Number of pages||4|
|Publication status||Published - 18 May 2017|
- Field programmable gate arrays
- Image segmentation
- Real-time systems
- Atom optics
Wang, M., Ng, H-C., Chung, B. MF., Varma, B. S. C., Jaiswal, M. K., Tsia, K. K., Shum, H. C., & So, H. K-H. (2017). Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. 261-264. https://doi.org/10.1109/FPT.2016.7929548