A saliency-based bottom-up visual attention model for dynamic scenes analysis

Biological Cybernetics ◽

10.1007/s00422-012-0542-2 ◽

2013 ◽

Vol 107 (2) ◽

pp. 141-160 ◽

Author(s):

David F. Ramirez-Moreno ◽

Odelia Schwartz ◽

Juan F. Ramirez-Villegas

Keyword(s):

Visual Attention ◽

Dynamic Scenes ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model

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Bottom-up visual attention model based on FPGA

2012 19th IEEE International Conference on Electronics, Circuits, and Systems (ICECS 2012) ◽

10.1109/icecs.2012.6463735 ◽

2012 ◽

Author(s):

Francisco Barranco ◽

Javier Diaz ◽

Begona Pino ◽

Eduardo Ros

Keyword(s):

Visual Attention ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model ◽

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A visual attention model combining top-down and bottom-up mechanisms for salient object detection

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2011.5946648 ◽

2011 ◽

Author(s):

Yuming Fang ◽

Weisi Lin ◽

Chiew Tong Lau ◽

Bu-Sung Lee

Keyword(s):

Visual Attention ◽

Object Detection ◽

Salient Object Detection ◽

Salient Object ◽

Top Down ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model ◽

Model Combining

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A random center surround bottom up visual attention model useful for salient region detection

2011 IEEE Workshop on Applications of Computer Vision (WACV) ◽

10.1109/wacv.2011.5711499 ◽

2011 ◽

Author(s):

Tadmeri Narayan Vikram ◽

Marko Tscherepanow ◽

Britta Wrede

Keyword(s):

Visual Attention ◽

Bottom Up ◽

Salient Region Detection ◽

Visual Attention Model ◽

Attention Model ◽

Salient Region ◽

Region Detection

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A visual attention model for dynamic scenes based on motion features

2012 12th International Conference on Control Automation Robotics & Vision (ICARCV) ◽

10.1109/icarcv.2012.6485393 ◽

2012 ◽

Author(s):

Zhou Changle ◽

Chen Jiawei ◽

Yao Jinliang

Keyword(s):

Visual Attention ◽

Dynamic Scenes ◽

Visual Attention Model ◽

Attention Model ◽

Motion Features

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A Visual Attention Model for Dynamic Scenes Based on Two-Pathway Processing in Brain

2008 Fourth International Conference on Natural Computation ◽

10.1109/icnc.2008.392 ◽

2008 ◽

Author(s):

Jiawei Chen ◽

Changle Zhou ◽

Kunhui Lin ◽

Yanyun Qu

Keyword(s):

Visual Attention ◽

Dynamic Scenes ◽

Visual Attention Model ◽

Attention Model

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Bottom-up spatiotemporal visual attention model for video analysis

IET Image Processing ◽

10.1049/iet-ipr:20060040 ◽

2007 ◽

Vol 1 (2) ◽

pp. 237 ◽

Author(s):

K. Rapantzikos ◽

N. Tsapatsoulis ◽

Y. Avrithis ◽

S. Kollias

Keyword(s):

Visual Attention ◽

Video Analysis ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model

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Image Content Extraction Using a Bottom-Up Visual Attention Model

10.1109/icds.2009.32 ◽

2009 ◽

Author(s):

Ionut Pirnog ◽

Cristina Oprea ◽

Constantin Paleologu

Keyword(s):

Visual Attention ◽

Image Content ◽

Bottom Up ◽

Content Extraction ◽

Visual Attention Model ◽

Attention Model

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Objective evaluation of fabric pilling based on bottom-up visual attention model

Journal of the Textile Institute ◽

10.1080/00405000.2016.1176623 ◽

2016 ◽

Vol 108 (4) ◽

pp. 597-604 ◽

Author(s):

Shengqi Guan ◽

Hongyu Shi ◽

Yanqiang Qi

Keyword(s):

Visual Attention ◽

Objective Evaluation ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model

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A Pulse-Coupled Neural Network as A Simplified Bottom-Up Visual Attention Model

2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06) ◽

10.1109/sbrn.2006.8 ◽

2006 ◽

Author(s):

Marcos Quiles ◽

Roseli Romero ◽

Liang Zhao

Keyword(s):

Neural Network ◽

Visual Attention ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model ◽

Pulse Coupled Neural Network

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A Top-Down and Bottom-Up Visual Attention Model for Humanoid Object Approaching and Obstacle Avoidance

2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR) ◽

10.1109/lars-sbr.2016.12 ◽

2016 ◽

Author(s):

Hendry Ferreira Chame ◽

Christine Chevallereau

Keyword(s):

Visual Attention ◽

Obstacle Avoidance ◽

Top Down ◽

Bottom Up ◽

Visual Attention Model ◽

Attention Model

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