2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Peter Corcoran

2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Electrical & Electronic Engineering

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

Consumer-level multi-array cameras are a key enabling technology for next generation smartphones imaging systems. The present paper aims to analyze the accuracy of the depth estimation while using different camera combinations in a multi-array camera. This is done by providing a framework of deep neural networks to determine depth map from a sequence of images captured by a multi-array camera. Capturing depth information enables users to perform a range of post-capture edits such as refocusing, and creating a 3D model of any scene. Thus it is essential to calculate an accurate depth map while using the minimum computational resources.

Original language	English (Ireland)
Title of host publication	The Application of Deep Learning on Depth from Multi-Array Camera
Publication status	Published - 1 Jan 2018

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Javidnia, H,Bazrafkan, S,Corcoran, P,Mohanty, SP,Corcoran, P,Li, H,Sengupta, A,Lee, JH

Cite this

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title = "2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)",

abstract = "Consumer-level multi-array cameras are a key enabling technology for next generation smartphones imaging systems. The present paper aims to analyze the accuracy of the depth estimation while using different camera combinations in a multi-array camera. This is done by providing a framework of deep neural networks to determine depth map from a sequence of images captured by a multi-array camera. Capturing depth information enables users to perform a range of post-capture edits such as refocusing, and creating a 3D model of any scene. Thus it is essential to calculate an accurate depth map while using the minimum computational resources.",

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N2 - Consumer-level multi-array cameras are a key enabling technology for next generation smartphones imaging systems. The present paper aims to analyze the accuracy of the depth estimation while using different camera combinations in a multi-array camera. This is done by providing a framework of deep neural networks to determine depth map from a sequence of images captured by a multi-array camera. Capturing depth information enables users to perform a range of post-capture edits such as refocusing, and creating a 3D model of any scene. Thus it is essential to calculate an accurate depth map while using the minimum computational resources.

AB - Consumer-level multi-array cameras are a key enabling technology for next generation smartphones imaging systems. The present paper aims to analyze the accuracy of the depth estimation while using different camera combinations in a multi-array camera. This is done by providing a framework of deep neural networks to determine depth map from a sequence of images captured by a multi-array camera. Capturing depth information enables users to perform a range of post-capture edits such as refocusing, and creating a 3D model of any scene. Thus it is essential to calculate an accurate depth map while using the minimum computational resources.

M3 - Conference Publication

BT - The Application of Deep Learning on Depth from Multi-Array Camera

ER -

2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this