Bio-inspired Stereo Vision System with Silicon Retina Imagers

Kogler, Jürgen; Sulzbachner, Christoph; Kubinger, Wilfried

doi:10.1007/978-3-642-04667-4_18

Cited by 46 publications

(29 citation statements)

References 7 publications

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“…The availability of these event-based neuromorphic sensors has led to an increased interest in studying and developing a new class of event-based vision algorithms121314151617. However, most of these algorithms have been used in conjunction with standard computing architectures, rather than neuromorphic processors, and to a large extent are still biased by the frame-based approach typically adopted in classical machine vision.…”

mentioning

confidence: 99%

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

Osswald

Ieng

Benosman

et al. 2017

Sci Rep

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Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems.

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mentioning

confidence: 99%

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

Osswald

Ieng

Benosman

et al. 2017

Sci Rep

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“…An evaluation of state-of-the-art stereo matching algorithms including both area-and feature-based approaches revealed that such methods cannot exploit the main characteristics of the Silicon Retina sensor technology properly [6].…”

Section: Real-time Stereo Visionmentioning

confidence: 99%

“…The aim of developing algorithms for these types of sensors is processing the asynchronous data directly, as it is delivered by the imagers. Reconstructing classical frame-based image representations from the asynchronous sensor data in memory combined with using conventional image processing algorithms throttles the performance significantly [6].…”

Section: Introductionmentioning

confidence: 99%

An optimized Silicon Retina stereo matching algorithm using time-space correlation

Sulzbachner

Zinner

Kogler

2011

CVPR 2011 Workshops

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This paper presents an optimized implementation of a Silicon Retina based stereo matching algorithm using timespace correlation. The algorithm combines an event-based time correlation approach with a census transform based matching method on grayscale images that are generated from the sensor output. The data processing part of the system is optimized for an Intel i7 mobile architecture and a C64x+ multi-core digital signal processor (DSP). Both platforms use an additional C64x+ single-core DSP system for acquisition and pre-processing of sensor data. We focus on the performance optimization techniques that had a major impact on the run-time performance of both processor architectures used.

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“…Various stereo matching algorithms for stereo DVS camera setups have been proposed. (47)(48)(49)(50) Sulzbachner et al proposed an address event frame, a collection of events over a defined time period, based on the correlation method for stereo matching algorithms. (47) Rogister et al proposed an asynchronous event-based binocular stereo matching algorithm combining epipolar geometry and timing information.…”

Section: Stereo Matchingmentioning

confidence: 99%

A Review of Bioinspired Vision Sensors and Their Applications

Cho¹,

“Dan”²,

Lee³

2015

Sensors and Materials

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Bioinspired vision sensors have become very attractive in recent years because of their inherent redundancy suppression, integrated processing, fast sensing capability, wide dynamic range, and low power consumption. These sensors combine functionalities of the biological "where" and "what" systems of the human visual system and process the visual information using an asynchronous event-driven method. Since the emergence of bioinspired vision sensors, various applications based on them have been proposed in the computer vision and robotics fields. In this paper, we review bioinspired vision sensors and their applications. The reviewed sensors include dynamic vision sensors (DVSs), asynchronous time-based image sensors (ATISs), and dynamic and active pixel vision sensors (DAVISs). The reviewed applications based on the bioinspired vision sensors include visual tracking, detection, recognition, simultaneous localization and mapping (SLAM), reconstruction, stereo matching, and control.

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Bio-inspired Stereo Vision System with Silicon Retina Imagers

Cited by 46 publications

References 7 publications

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

An optimized Silicon Retina stereo matching algorithm using time-space correlation

A Review of Bioinspired Vision Sensors and Their Applications

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