Computational Models of Human Visual Attention and Their Implementations: A Survey

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概要

• 論文の詳細を見る

• We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Hirayama Takatsugu

  Graduate School Of Informatics Kyoto University

• Kimura Akisato

  NTT Communication Science Laboratories, NTT Corporation

• Yonetani Ryo

  Graduate School of Informatics, Kyoto University

• HIRAYAMA Takatsugu

  Graduate School of Information Science, Nagoya University

関連論文

• Estimates of User Interest Using Timing Structures between Proactive Content-Display Updates and Eye Movements
• Mental Focus Analysis Using the Spatio-temporal Correlation between Visual Saliency and Eye Movements
• Mental Focus Analysis Using the Spatio-temporal Correlation between Visual Saliency and Eye Movements
• Interest Point Detection Based on Stochastically Derived Stability
• Learning Spatiotemporal Gaps between Where We Look and What We Focus on
• SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
• Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression
• SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
• Interest Point Detection Based on Stochastically Derived Stability
• Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression
• Computational Models of Human Visual Attention and Their Implementations: A Survey
• Learning Spatiotemporal Gaps between Where We Look and What We Focus on
• Computational Models of Human Visual Attention and Their Implementations : A Survey

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