A fast region of interest extraction approach based on stentiford model of visual attention

2010 ◽  
Author(s):  
Zhang Jing ◽  
Zhuo Li ◽  
Liu Zhixing ◽  
Sui Lei
Keyword(s):  
Visual Attention ◽  
Region Of Interest ◽  
Fast Region

scholarly journals Covert Visual Attention Modulates Face-Specific Activity in the Human Fusiform Gyrus: fMRI Study

1998 ◽  
Vol 79 (3) ◽  
pp. 1574-1578 ◽  
Author(s):  
Ewa Wojciulik ◽  
Nancy Kanwisher ◽  
Jon Driver
Keyword(s):  
Visual Attention ◽  
Specific Activity ◽  
Region Of Interest ◽  
Fusiform Gyrus ◽  
Covert Attention ◽  
Matching Task ◽  
Voluntary Attention ◽  
Retinal Stimulation ◽  
Covert Visual Attention ◽  
Fmri Study

Wojciulik, Ewa, Nancy Kanwisher, and Jon Driver. Covert visual attention modulates face-specific activity in the human fusiform gyrus: an fMRI study. J. Neurophysiol. 79: 1574–1578, 1998. Several lines of evidence demonstrate that faces undergo specialized processing within the primate visual system. It has been claimed that dedicated modules for such biologically significant stimuli operate in a mandatory fashion whenever their triggering input is presented. However, the possible role of covert attention to the activating stimulus has never been examined for such cases. We used functional magnetic resonance imaging to test whether face-specific activity in the human fusiform face area (FFA) is modulated by covert attention. The FFA was first identified individually in each subject as the ventral occipitotemporal region that responded more strongly to visually presented faces than to other visual objects under passive central viewing. This then served as the region of interest within which attentional modulation was tested independently, using active tasks and a very different stimulus set. Subjects viewed brief displays each comprising two peripheral faces and two peripheral houses (all presented simultaneously). They performed a matching task on either the two faces or the two houses, while maintaining central fixation to equate retinal stimulation across tasks. Signal intensity was reliably stronger during face-matching than house matching in both right- and left-hemisphere predefined FFAs. These results show that face-specific fusiform activity is reduced when stimuli appear outside (vs. inside) the focus of attention. Despite the modular nature of the FFA (i.e., its functional specificity and anatomic localization), face processing in this region nonetheless depends on voluntary attention.

Research of Image Processing Algorithm Based on Region of Interest

2013 ◽  
Vol 798-799 ◽  
pp. 814-817
Author(s):  
Fang Wang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Attention ◽  
Region Of Interest ◽  
Processing Algorithm ◽  
Human Information Processing ◽  
Visual Attention Model ◽  
Attention Model ◽  
Starting Point ◽  
Image Zooming

With the further development of modern scientific study, it promotes the research of the image based on region of interest. By doing these studies, it satisfies the pressing needs in many fields such as military, production and living areas, etc. meanwhile, it is also the key problem in the fields of computer vision, image processing, artificial intelligence, video communication. Visual attention plays a very important role in the human information processing of the psychological adjustment mechanism. It is a conscious activity which chooses the useful information from large amounts of information. It owns the high efficiency and reliability in the process of human visual perception. Visual attention model, which is based on the visual attention and combined with the computer vision, builds a spatial feature of visual attention architecture. It is helpful not only to find out the visual cognition rule, but also to solve the problem of interested area selection and focus on improving the efficiency of the computer image processing. It has important application value in areas such as image extraction and image zooming. The paper has carried out the deeply study in the interested image region. With the improved visual attention model as a starting point, it combines with graph processing algorithm. And it uses the image extraction algorithm and image zooming algorithm to improve the visual attention model and detect the interested area.

scholarly journals Scanpath Prediction for Visual Attention using IOR-ROI LSTM

2018 ◽  
Author(s):  
Zhenzhong Chen ◽  
Wanjie Sun
Keyword(s):  
Visual Attention ◽  
Short Term Memory ◽  
Region Of Interest ◽  
Image Features ◽  
Superior Performance ◽  
Gaze Shift ◽  
Shift Behavior ◽  
Proposed Model ◽  
Scene Information ◽  
Core Part

Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i.e., an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for predicting the next ROI given the inhibited image features. Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths.

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