Implementation of Visual Attention System Using Bottom-up Saliency Map Model

2003 ◽  
pp. 678-685 ◽  
Author(s):  
Sang-Jae Park ◽  
Sang-Woo Ban ◽  
Jang-Kyoo Shin ◽  
Minho Lee
Keyword(s):  
Visual Attention ◽  
Saliency Map ◽  
Bottom Up ◽  
Attention System

scholarly journals Bottom-up visual attention model for still image: a preliminary study

2020 ◽  
Vol 6 (1) ◽  
pp. 82
Author(s):  
Adhi Prahara ◽  
Murinto Murinto ◽  
Dewi Pramudi Ismi
Keyword(s):  
Visual Attention ◽  
Object Detection ◽  
Video Compression ◽  
Saliency Map ◽  
Bottom Up ◽  
Attention Model ◽  
Intrinsic Cues ◽  
Preliminary Study ◽  
Segmentation Image ◽  
Human Visual Attention

The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.

Influence of Movement Expertise on Visual Perception of Objects, Events and Motor Action

2012 ◽  
pp. 1-30 ◽  
Author(s):  
Kai Essig ◽  
Oleg Strogan ◽  
Helge Ritter ◽  
Thomas Schack
Keyword(s):  
Eye Movements ◽  
Visual Perception ◽  
Visual Attention ◽  
Saliency Map ◽  
Long Term Memory ◽  
Bottom Up ◽  
Term Memory ◽  
Perceptual Skills ◽  
Control Learning

Various computational models of visual attention rely on the extraction of salient points or proto-objects, i.e., discrete units of attention, computed from bottom-up image features. In recent years, different solutions integrating top-down mechanisms were implemented, as research has shown that although eye movements initially are solely influenced by bottom-up information, after some time goal driven (high-level) processes dominate the guidance of visual attention towards regions of interest (Hwang, Higgins & Pomplun, 2009). However, even these improved modeling approaches are unlikely to generalize to a broader range of application contexts, because basic principles of visual attention, such as cognitive control, learning and expertise, have thus far not sufficiently been taken into account (Tatler, Hayhoe, Land & Ballard, 2011). In some recent work, the authors showed the functional role and representational nature of long-term memory structures for human perceptual skills and motor control. Based on these findings, the chapter extends a widely applied saliency-based model of visual attention (Walther & Koch, 2006) in two ways: first, it computes the saliency map using the cognitive visual attention approach (CVA) that shows a correspondence between regions of high saliency values and regions of visual interest indicated by participants’ eye movements (Oyekoya & Stentiford, 2004). Second, it adds an expertise-based component (Schack, 2012) to represent the influence of the quality of mental representation structures in long-term memory (LTM) and the roles of learning on the visual perception of objects, events, and motor actions.

scholarly journals An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

2020 ◽  
Vol 12 (5) ◽  
pp. 781 ◽  
Author(s):  
Yaochen Liu ◽  
Lili Dong ◽  
Yang Chen ◽  
Wenhai Xu
Keyword(s):  
Visual Attention ◽  
Image Fusion ◽  
Feature Fusion ◽  
Saliency Map ◽  
Human Vision ◽  
Fusion Algorithm ◽  
Visible Image ◽  
Fusion Strategy ◽  
Fair Competition ◽  
Attention System

Infrared and visible image fusion technology provides many benefits for human vision and computer image processing tasks, including enriched useful information and enhanced surveillance capabilities. However, existing fusion algorithms have faced a great challenge to effectively integrate visual features from complex source images. In this paper, we design a novel infrared and visible image fusion algorithm based on visual attention technology, in which a special visual attention system and a feature fusion strategy based on the saliency maps are proposed. Special visual attention system first utilizes the co-occurrence matrix to calculate the image texture complication, which can select a particular modality to compute a saliency map. Moreover, we improved the iterative operator of the original visual attention model (VAM), a fair competition mechanism is designed to ensure that the visual feature in detail regions can be extracted accurately. For the feature fusion strategy, we use the obtained saliency map to combine the visual attention features, and appropriately enhance the tiny features to ensure that the weak targets can be observed. Different from the general fusion algorithm, the proposed algorithm not only preserve the interesting region but also contain rich tiny details, which can improve the visual ability of human and computer. Moreover, experimental results in complicated ambient conditions show that the proposed algorithm in this paper outperforms state-of-the-art algorithms in both qualitative and quantitative evaluations, and this study can extend to the field of other-type image fusion.

scholarly journals Saliency-Based Gaze Visualization for Eye Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5178
Author(s):  
Sangbong Yoo ◽  
Seongmin Jeong ◽  
Seokyeon Kim ◽  
Yun Jang
Keyword(s):  
Eye Movements ◽  
Visual Attention ◽  
Eye Movement ◽  
Visual Stimuli ◽  
Saliency Map ◽  
Gaze Behavior ◽  
The Gaze ◽  
Movement Data ◽  
Visual Clues ◽  
Human Visual Attention

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.

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