The Experts below are selected from a list of 180954 Experts worldwide ranked by ideXlab platform
Xiongkuo Min - One of the best experts on this subject based on the ideXlab platform.
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Visual Attention analysis and prediction on human faces
Information Sciences, 2017Co-Authors: Xiongkuo Min, Guangtao Zhai, Jing Liu, Shiqi Wang, Xinfeng Zhang, Xiaokang YangAbstract:Abstract Human faces are almost always the focus of Visual Attention because of the rich semantic information therein. While some Visual Attention models incorporating face cues indeed perform better in images with faces, yet there is no systematic analysis of the deployment of Visual Attention on human faces in the context of Visual Attention modelling, nor is there any specific Attention model designed for face images. On faces, many high-level factors have influence on Visual Attention. To investigate Visual Attention on human faces, we first construct a Visual Attention database for Faces (VAF database), which is composed of 481 face images along with eye-tracking data of 22 viewers. Statistics of the eye-movement data show that some high-level factors such as face size, facial features and face pose have impact on Visual Attention. Thus we propose to build Visual Attention models specifically for face images through combining low-level saliency calculated by traditional saliency models with high-level facial features. Efficiency of the built models is verified on the VAF database. When combined with high-level facial features, most saliency models can achieve better performance.
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VCIP - Visual Attention analysis and prediction on human faces with mole
2016 Visual Communications and Image Processing (VCIP), 2016Co-Authors: Qianqian Wei, Guangtao Zhai, Xiongkuo MinAbstract:Nowadays Visual Attention has been applied to many research and application problems. Different algorithms from low level to high level have been developed to detect the saliency map. For images with human face, high-level factors like mole may influence the Visual Attention. To investigate Visual Attention on human face with mole, we construct a Visual Attention database for Faces with Mole (VAFM) that contains face images, fixation density maps (FDM), landmark points as well as eye tracking data. Then we build Visual Attention model for face images with mole combining low-level saliency algorithms and high-level feature. Compared with the traditional low-level saliency algorithms, the proposed model perform better on our dataset.
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VCIP - Visual Attention on human face
2015 Visual Communications and Image Processing (VCIP), 2015Co-Authors: Xiongkuo Min, Guangtao ZhaiAbstract:Human faces are always the focus of Visual Attention since faces can provide plenty of information. Although some Visual Attention models incorporating face cues work better in scenes containing faces, no Visual Attention model is particularly designed for faces. On faces, many high-level factors will influence Visual Attention distribution. In practice, there are many Visual communication systems in which faces occupy the scenes, such as video calls. Specific Visual Attention model designed for face images will be of great value in these circumstances. In this paper, we conduct research on Visual Attention analysis and modelling on human faces. To facilitate this research, we collect 120 face images and perform eye-tracking experiments with these images. Eye-movement data shows that detailed Visual Attention allocation exists on faces. Using face detection and facial landmark localization, we find that some facial features are highly effective for Visual Attention prediction. The performance of many Visual Attention models can be improved by incorporating those facial features.
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ICME - Influence of compression artifacts on Visual Attention
2014 IEEE International Conference on Multimedia and Expo (ICME), 2014Co-Authors: Xiongkuo Min, Guangtao Zhai, Zhongpai GaoAbstract:Visual Attention is an important function of the human Visual system (HVS). In the long term research of Visual Attention, various computational models have been proposed with encouraging results. However, most of those work were conducted on images with ideal Visual quality. In practice, outputs of most Visual communication systems contain different levels of artifacts, e.g. noise, blurring, blockiness and etc. Therefore, it is interesting to investigate the impacts of artifacts on Visual Attention. In this paper, we question into the problem of how the widely encountered JPEG compression artifacts affect Visual Attention. We designed eye-tracking experiments on images with different levels of compression and viewing time and quantitatively compared the recorded eye movement data. We found that compression level does have impacts on Visual Attention, and yet this influence can be negligible for low levels of compression. For high levels of compression, the Visual artifacts alter Visual Attention in a systematic way. Dependence of the influence on viewing duration was also analyzed and it was observed that too short or too long viewing time reduces the impact of compression artifacts on Visual Attention.
Olivier Droulers - One of the best experts on this subject based on the ideXlab platform.
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Front of pack symmetry influences Visual Attention
Journal of Retailing and Consumer Services, 2020Co-Authors: S. Lacoste-badie, Arnaud Bigoin-gagnan, Olivier DroulersAbstract:This paper investigates the impact on Visual Attention of a symmetrical versus an asymmetrical arrangement on the front of pack (FOP) of FMCGs. The authors conducted a laboratory experiment using an eye-tracking method. Two FOPs were designed for each product category (orange juice, chocolate bars, pasta and biscuits). In one version the information items were arranged symmetrically around a vertical axis, and in another they were asymmetrically arranged. The findings show that symmetry influences viewers' Attention, first by influencing the Visual Attention paid to the entire FOP and, second, by its impact on the capacity of specific FOP areas to capture and hold Visual Attention.
Guangtao Zhai - One of the best experts on this subject based on the ideXlab platform.
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Visual Attention analysis and prediction on human faces
Information Sciences, 2017Co-Authors: Xiongkuo Min, Guangtao Zhai, Jing Liu, Shiqi Wang, Xinfeng Zhang, Xiaokang YangAbstract:Abstract Human faces are almost always the focus of Visual Attention because of the rich semantic information therein. While some Visual Attention models incorporating face cues indeed perform better in images with faces, yet there is no systematic analysis of the deployment of Visual Attention on human faces in the context of Visual Attention modelling, nor is there any specific Attention model designed for face images. On faces, many high-level factors have influence on Visual Attention. To investigate Visual Attention on human faces, we first construct a Visual Attention database for Faces (VAF database), which is composed of 481 face images along with eye-tracking data of 22 viewers. Statistics of the eye-movement data show that some high-level factors such as face size, facial features and face pose have impact on Visual Attention. Thus we propose to build Visual Attention models specifically for face images through combining low-level saliency calculated by traditional saliency models with high-level facial features. Efficiency of the built models is verified on the VAF database. When combined with high-level facial features, most saliency models can achieve better performance.
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VCIP - Visual Attention analysis and prediction on human faces with mole
2016 Visual Communications and Image Processing (VCIP), 2016Co-Authors: Qianqian Wei, Guangtao Zhai, Xiongkuo MinAbstract:Nowadays Visual Attention has been applied to many research and application problems. Different algorithms from low level to high level have been developed to detect the saliency map. For images with human face, high-level factors like mole may influence the Visual Attention. To investigate Visual Attention on human face with mole, we construct a Visual Attention database for Faces with Mole (VAFM) that contains face images, fixation density maps (FDM), landmark points as well as eye tracking data. Then we build Visual Attention model for face images with mole combining low-level saliency algorithms and high-level feature. Compared with the traditional low-level saliency algorithms, the proposed model perform better on our dataset.
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VCIP - Visual Attention on human face
2015 Visual Communications and Image Processing (VCIP), 2015Co-Authors: Xiongkuo Min, Guangtao ZhaiAbstract:Human faces are always the focus of Visual Attention since faces can provide plenty of information. Although some Visual Attention models incorporating face cues work better in scenes containing faces, no Visual Attention model is particularly designed for faces. On faces, many high-level factors will influence Visual Attention distribution. In practice, there are many Visual communication systems in which faces occupy the scenes, such as video calls. Specific Visual Attention model designed for face images will be of great value in these circumstances. In this paper, we conduct research on Visual Attention analysis and modelling on human faces. To facilitate this research, we collect 120 face images and perform eye-tracking experiments with these images. Eye-movement data shows that detailed Visual Attention allocation exists on faces. Using face detection and facial landmark localization, we find that some facial features are highly effective for Visual Attention prediction. The performance of many Visual Attention models can be improved by incorporating those facial features.
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ICME - Influence of compression artifacts on Visual Attention
2014 IEEE International Conference on Multimedia and Expo (ICME), 2014Co-Authors: Xiongkuo Min, Guangtao Zhai, Zhongpai GaoAbstract:Visual Attention is an important function of the human Visual system (HVS). In the long term research of Visual Attention, various computational models have been proposed with encouraging results. However, most of those work were conducted on images with ideal Visual quality. In practice, outputs of most Visual communication systems contain different levels of artifacts, e.g. noise, blurring, blockiness and etc. Therefore, it is interesting to investigate the impacts of artifacts on Visual Attention. In this paper, we question into the problem of how the widely encountered JPEG compression artifacts affect Visual Attention. We designed eye-tracking experiments on images with different levels of compression and viewing time and quantitatively compared the recorded eye movement data. We found that compression level does have impacts on Visual Attention, and yet this influence can be negligible for low levels of compression. For high levels of compression, the Visual artifacts alter Visual Attention in a systematic way. Dependence of the influence on viewing duration was also analyzed and it was observed that too short or too long viewing time reduces the impact of compression artifacts on Visual Attention.
S. Lacoste-badie - One of the best experts on this subject based on the ideXlab platform.
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Front of pack symmetry influences Visual Attention
Journal of Retailing and Consumer Services, 2020Co-Authors: S. Lacoste-badie, Arnaud Bigoin-gagnan, Olivier DroulersAbstract:This paper investigates the impact on Visual Attention of a symmetrical versus an asymmetrical arrangement on the front of pack (FOP) of FMCGs. The authors conducted a laboratory experiment using an eye-tracking method. Two FOPs were designed for each product category (orange juice, chocolate bars, pasta and biscuits). In one version the information items were arranged symmetrically around a vertical axis, and in another they were asymmetrically arranged. The findings show that symmetry influences viewers' Attention, first by influencing the Visual Attention paid to the entire FOP and, second, by its impact on the capacity of specific FOP areas to capture and hold Visual Attention.
Andrea Guzzetta - One of the best experts on this subject based on the ideXlab platform.
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Development of Visual Attention in West syndrome.
Epilepsia, 2002Co-Authors: Francesco Guzzetta, Maria Flavia Frisone, Daniela Ricci, Teresa Randò, Andrea GuzzettaAbstract:Summary: Purpose: To study prospectively the evolution of Visual Attention in children with West syndrome to evaluate its development before the onset of spasms, its possible deterioration as a consequence of epileptic disorders, and its outcome at the age of 2 years, and the possible relation between the impairment of Visual Attention and cognitive development. Methods: Infants with symptomatic West syndrome were examined before the onset of spasms and until age 24 ± 2 months. Visual Attention study (through a clinical observation and the fixation-shift test), cognitive assessment, and complete clinical examination including brain magnetic resonance imaging were performed. Results: A maturation defect of fixation shift skills was generally observed in infants with West syndrome. In some cases, the impaired Visual-Attention abilities paralleled a cognitive deterioration, even months before the onset of spasms. During the acute stage of West syndrome, infants lost the previously acquired Visual and cognitive abilities, with a typical fluctuation of arousal. Usually at 2 years, there was a persistent defective Visual Attention detected with the fixation-shift test. Conclusions: The parallel defect of Visual Attention and of cognitive competencies is a constant finding in infants with West syndrome; these can precede the clinical onset of epileptic spasms. The severity and persistence of Visual inAttention might be explained by the age of Visual maturation, corresponding to the usual onset of West syndrome.
