Applications of Saliency Models

Matei Mancas 1 Olivier Le Meur 2
2 Sirocco - Analysis representation, compression and communication of visual data
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique
Abstract : This chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models. There are numerous applications and we try here to remain as exhaustive as possible to provide a picture of all the applications of saliency models, but also to detect where future developments might be of interest. The applications are grouped into three categories. The first one uses the detection of the most important regions in an image and contains applications such as video surveillance, audio surveillance, defect detection, pathology detection, expressive and social gestures, computer graphics and quality metrics. The second category uses saliency maps to detect the regions which are the less interesting in an image. Here one can found applications like texture metrics, compression, retargeting, summarization, watermarking and attention-based ad insertion. Finally, a third category uses the most interesting areas in an image with further processing like comparisons between those areas. In this category one can find image registration and landmarks, object recognition, action guidance in robotics or avatars, web sites optimization, images memorability, best viewpoint, symmetries and automatic focus on images.
Type de document :
Chapitre d'ouvrage
Matei Mancas; Vincent P. Ferrera; Nicolas Riche; John G. Taylor. From Human Attention to Computational Attention. A Multidisciplinary Approach, 10, Springer, pp.331-377, 2016, Springer Series in Cognitive and Neural Systems, 978-1-4939-3435-5. 〈10.1007/978-1-4939-3435-5_18〉
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https://hal.inria.fr/hal-01393254
Contributeur : Olivier Le Meur <>
Soumis le : lundi 7 novembre 2016 - 10:42:07
Dernière modification le : mercredi 21 février 2018 - 01:54:50

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Matei Mancas, Olivier Le Meur. Applications of Saliency Models. Matei Mancas; Vincent P. Ferrera; Nicolas Riche; John G. Taylor. From Human Attention to Computational Attention. A Multidisciplinary Approach, 10, Springer, pp.331-377, 2016, Springer Series in Cognitive and Neural Systems, 978-1-4939-3435-5. 〈10.1007/978-1-4939-3435-5_18〉. 〈hal-01393254〉

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