Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition

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Resumé

Object recognition is one of the important tasks in computer vision which has found enormous applications.Depth modality is proven to provide supplementary information to the common RGB modality for objectrecognition. In this paper, we propose methods to improve the recognition performance of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show that the RGB stream of the FusionNetmodel can benefit from using deeper network architectures, namely the 16-layered VGGNet, in exchange forthe 8-layered CaffeNet. In combination, these changes improves the recognition performance with 2.2% incomparison to the original FusionNet, when evaluating on the Washington RGB-D Object Dataset.
OriginalsprogEngelsk
TitelInternational Joint Conference on Computational Intelligence
ForlagSCITEPRESS Digital Library
Publikationsdato2017
Sider121-128
ISBN (Trykt)978-989-758-274-5
DOI
StatusUdgivet - 2017
BegivenhedInternational Joint Conference on Computational Intelligence - Funchal, Portugal
Varighed: 1 nov. 20173 nov. 2017
Konferencens nummer: 9
http://www.ijcci.org/

Konference

KonferenceInternational Joint Conference on Computational Intelligence
Nummer9
LandPortugal
ByFunchal
Periode01/11/201703/11/2017
Internetadresse

Fingerprint

Object recognition
Processing
Network architecture
Computer vision

Citer dette

Aakerberg, A., Nasrollahi, K., Rasmussen, C. B., & Moeslund, T. B. (2017). Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition. I International Joint Conference on Computational Intelligence (s. 121-128). SCITEPRESS Digital Library. https://doi.org/10.5220/0006511501210128
Aakerberg, Andreas ; Nasrollahi, Kamal ; Rasmussen, Christoffer Bøgelund ; Moeslund, Thomas B. / Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition. International Joint Conference on Computational Intelligence. SCITEPRESS Digital Library, 2017. s. 121-128
@inproceedings{0400b36cce8b4e3cb1a0d911b1678321,
title = "Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition",
abstract = "Object recognition is one of the important tasks in computer vision which has found enormous applications.Depth modality is proven to provide supplementary information to the common RGB modality for objectrecognition. In this paper, we propose methods to improve the recognition performance of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show that the RGB stream of the FusionNetmodel can benefit from using deeper network architectures, namely the 16-layered VGGNet, in exchange forthe 8-layered CaffeNet. In combination, these changes improves the recognition performance with 2.2{\%} incomparison to the original FusionNet, when evaluating on the Washington RGB-D Object Dataset.",
keywords = "Deep Learning , Surface Normals, Computer Vision, Artificial Vision, RGB-D, Convolutional Neural Networks, TransferLearning",
author = "Andreas Aakerberg and Kamal Nasrollahi and Rasmussen, {Christoffer B{\o}gelund} and Moeslund, {Thomas B.}",
year = "2017",
doi = "10.5220/0006511501210128",
language = "English",
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booktitle = "International Joint Conference on Computational Intelligence",
publisher = "SCITEPRESS Digital Library",

}

Aakerberg, A, Nasrollahi, K, Rasmussen, CB & Moeslund, TB 2017, Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition. i International Joint Conference on Computational Intelligence. SCITEPRESS Digital Library, s. 121-128, International Joint Conference on Computational Intelligence, Funchal, Portugal, 01/11/2017. https://doi.org/10.5220/0006511501210128

Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition. / Aakerberg, Andreas; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund; Moeslund, Thomas B.

International Joint Conference on Computational Intelligence. SCITEPRESS Digital Library, 2017. s. 121-128.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition

AU - Aakerberg, Andreas

AU - Nasrollahi, Kamal

AU - Rasmussen, Christoffer Bøgelund

AU - Moeslund, Thomas B.

PY - 2017

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N2 - Object recognition is one of the important tasks in computer vision which has found enormous applications.Depth modality is proven to provide supplementary information to the common RGB modality for objectrecognition. In this paper, we propose methods to improve the recognition performance of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show that the RGB stream of the FusionNetmodel can benefit from using deeper network architectures, namely the 16-layered VGGNet, in exchange forthe 8-layered CaffeNet. In combination, these changes improves the recognition performance with 2.2% incomparison to the original FusionNet, when evaluating on the Washington RGB-D Object Dataset.

AB - Object recognition is one of the important tasks in computer vision which has found enormous applications.Depth modality is proven to provide supplementary information to the common RGB modality for objectrecognition. In this paper, we propose methods to improve the recognition performance of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show that the RGB stream of the FusionNetmodel can benefit from using deeper network architectures, namely the 16-layered VGGNet, in exchange forthe 8-layered CaffeNet. In combination, these changes improves the recognition performance with 2.2% incomparison to the original FusionNet, when evaluating on the Washington RGB-D Object Dataset.

KW - Deep Learning

KW - Surface Normals

KW - Computer Vision

KW - Artificial Vision

KW - RGB-D

KW - Convolutional Neural Networks

KW - TransferLearning

U2 - 10.5220/0006511501210128

DO - 10.5220/0006511501210128

M3 - Article in proceeding

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Aakerberg A, Nasrollahi K, Rasmussen CB, Moeslund TB. Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition. I International Joint Conference on Computational Intelligence. SCITEPRESS Digital Library. 2017. s. 121-128 https://doi.org/10.5220/0006511501210128