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Real-time depth from focus on a programmable focal plane processor


Martel, Julien N P; Muller, Lorenz K; Carey, Stephen J; Muller, Jonathan; Sandamirskaya, Yulia; Dudek, Piotr (2017). Real-time depth from focus on a programmable focal plane processor. IEEE Transactions on Circuits and Systems - Part I: Regular Papers, 65(3):925-934.

Abstract

Visual input can be used to recover the 3-D structure of a scene by estimating distances (depth) to the observer. Depth estimation is performed in various applications, such as robotics, autonomous driving, or surveillance. We present a low-power, compact, passive, and static imaging system that computes a semi-dense depth map in real time for a wide range of depths. This is achieved by using a focus-tunable liquid lens to sweep the optical power of the system at a high frequency, computing depth from focus on a mixed-signal programmable focal-plane processor. The use of local and highly parallel process- ing directly on the focal plane removes the sensor-processor bandwidth limitations typical in conventional imaging and processor technologies and allows real-time performance to be achieved.

Abstract

Visual input can be used to recover the 3-D structure of a scene by estimating distances (depth) to the observer. Depth estimation is performed in various applications, such as robotics, autonomous driving, or surveillance. We present a low-power, compact, passive, and static imaging system that computes a semi-dense depth map in real time for a wide range of depths. This is achieved by using a focus-tunable liquid lens to sweep the optical power of the system at a high frequency, computing depth from focus on a mixed-signal programmable focal-plane processor. The use of local and highly parallel process- ing directly on the focal plane removes the sensor-processor bandwidth limitations typical in conventional imaging and processor technologies and allows real-time performance to be achieved.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2017
Deposited On:01 Mar 2018 12:08
Last Modified:19 Aug 2018 14:46
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE Transactions on Circuits and Systems I: Regular Papers
ISSN:1057-7122
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/TCSI.2017.2753878
Official URL:http://ieeexplore.ieee.org/abstract/document/8071011/
Project Information:
  • : FunderSNSF
  • : Grant IDCRSII2_160756
  • : Project TitleHybrid CMOS/Memristive Neuromorphic Systems for Data Analytics
  • : FunderSNSF
  • : Grant ID205321_143947
  • : Project TitleBiological Information in Cortical Communication

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