Wenhao Ran received his PhD in the Institute of Semiconductors, Chinese Academy of Sciences in 2022. He is currently a postdoctoral researcher at the School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing. His current research focuses on flexible biomimic vision system with in-memory sensing and computing.
Zhuoran Wang received his PhD in the department of Mining and Materials Engineering from the McGill University, QC, Canada in 2017. In 2019 he joined the Institute of Photonic Sciences (ICFO), Barcelona, as a postdoctoral /Marie-Curie research fellow. He is currently a professor at the School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing. His current research focuses on flexible and infrared optoelectronic sensors for biomimic vision.
Guozhen Shen received his PhD degree in Chemistry from the University of Science and Technology of China. He is currently a professor at the School of Integrated Circuits and Electronics, Beijing Institute of Technology (BIT), and the director of the Institute of Flexible Electronics and Intelligent Manufacturing. Before joining BIT, he worked at Hanyang University (Korea), National Institute for Materials Science (Japan), University of Southern California (US), and Huazhong University of Science and Technology (China), the Institute of Semiconductors, CAS (China). His current research focuses on flexible electronic devices for artificial intelligence and healthcare monitoring.
Fig. 1.
(Color online) (a) Cross-sectional optical microscope image of the central fovea in a peregrine falcon’s retina. (b) Optical camera image of a cross-sectional view of the artificial fovea. (c) Image simulation results showing the difference in motion in the two regions. Siemens star object is magnified and focused by the artificial fovea in the foveal region. (d) False-colored cross-sectional SEM image of an individual pixel in the multispectral image sensor. (e) Transient photoresponse of an individual pixel under different wavelengths. (f) Colored images obtained from each detector of the multispectral image sensor. The object distance is 200 mm. Scale bars, 2 mm. (g) Reconstructed image by combining color information obtained from each detector of the multispectral image sensor. The inset shows an optical camera image of the object. Scale bar, 2 mm. (h) Optical camera image of 8 pixels by 8 pixels of the multispectral image sensor. Optical microscope images of pixels in the foveal region (red box) and peripheral region (blue box). DF and DP represent the distance between each pixel in the foveal region and peripheral region, respectively. (i) Optical simulation results showing the difference in the image according to the pixel arrangement: foveated density and uniform density. (j) and (k) Simulation results showing object recognition capability of the original image and foveated vision based on YOLO v5. (l) Scatter plot of confidence for foveated vision and original image during 150 frames. (m) and (n) Simulation results showing the motion detection capability of the original image and foveated vision with optic flow based on the Farneback algorithm. The white circle presents the image center, and the arrow depicts visualized motion. (o) Scatter plot of the magnitude of motion within the center of images for the 52nd frame (indicated by white circles in (m) and (n)). Copyright 2024, American Association for the Advancement of Science[5].
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