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Extending 2D Saliency Models for Head Movement Prediction in 360-degree Images using CNN-based Fusion

Auteurs: » Djemai Ibrahim
» Hamidouche Wassim
» Deforges Olivier
Type : Conférence Internationale
Nom de la conférence : IEEE International Symposium on Circuits and Systems (ISCAS)
Lieu : Pays:
Lien : »
Publié le : 12-10-2020

Saliency prediction can be of great benefit for 360-degree image/video applications, including compression, streaming, rendering and viewpoint guidance. It is therefore quite natural to adapt the 2D saliency prediction methods for 360-degree images. To achieve this, it is necessary to project the 360-degree image to 2D plane. However, the existing projection techniques introduce different distortions, which provides poor results and makes inefficient the direct application of 2D saliency prediction models to 360-degree content. Consequently, in this paper, we propose a new framework for effectively applying any 2D saliency prediction method to 360-degree images. The proposed framework particularly includes a novel convolutional neural network based fusion approach that provides more accurate saliency prediction while avoiding the introduction of distortions. The proposed framework has been evaluated with â€¦

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