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Title: Event-based stereo depth estimation using belief propagation
Authors: Xie, Z
Chen, S
Orchard, G 
Keywords: algorithm
analytic method
depth perception
functional assessment
intermethod comparison
measurement accuracy
perceptive discrimination
visual evoked potential
visual system function
Issue Date: 2017
Citation: Xie, Z, Chen, S, Orchard, G (2017). Event-based stereo depth estimation using belief propagation. Frontiers in Neuroscience 11 (OCT) : 535. ScholarBank@NUS Repository.
Abstract: Compared to standard frame-based cameras, biologically-inspired event-based sensors capture visual information with low latency and minimal redundancy. These event-based sensors are also far less prone to motion blur than traditional cameras, and still operate effectively in high dynamic range scenes. However, classical framed-based algorithms are not typically suitable for these event-based data and new processing algorithms are required. This paper focuses on the problem of depth estimation from a stereo pair of event-based sensors. A fully event-based stereo depth estimation algorithm which relies on message passing is proposed. The algorithm not only considers the properties of a single event but also uses a Markov Random Field (MRF) to consider the constraints between the nearby events, such as disparity uniqueness and depth continuity. The method is tested on five different scenes and compared to other state-of-art event-based stereo matching methods. The results show that the method detects more stereo matches than other methods, with each match having a higher accuracy. The method can operate in an event-driven manner where depths are reported for individual events as they are received, or the network can be queried at any time to generate a sparse depth frame which represents the current state of the network. © 2017 Xie, Chen and Orchard.
Source Title: Frontiers in Neuroscience
ISSN: 1662-4548
DOI: 10.3389/fnins.2017.00535
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