Extraction of Visual Attention with Gaze Duration and Saliency Map

2006 ◽  
Author(s):  
H. Igarashi ◽  
S. Suzuki ◽  
T. Sugita ◽  
M. Kurisu ◽  
M. Kakikura
Keyword(s):  
Visual Attention ◽  
Saliency Map

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.

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.

Extraction of visual attention with gaze duration and saliency map

2006 ◽  
Author(s):  
Hiroshi Igarashi ◽  
Satoshi Suzuki ◽  
Tetsuro Sugita ◽  
Masamitsu Kurisu ◽  
Masayoshi Kakikura
Keyword(s):  
Visual Attention ◽  
Saliency Map

The Application of Visual Attention Mechanism in Road Disaster Identification and early Warning System

2013 ◽  
Vol 385-386 ◽  
pp. 523-526
Author(s):  
Shu Yue Hua ◽  
Nan Feng Xiao
Keyword(s):  
Visual Attention ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Saliency Map ◽  
Attention Mechanism ◽  
Total System ◽  
Time Performance ◽  
Disaster Monitoring ◽  
Visual Attention Mechanism

Visual attention mechanism is introduced into the traditional road disaster monitoring and early warning system. In this system, the disaster region is the focus of attention (FOA), which happens to be the object needed to process. Ittis algorithm [1]was used to extract the saliency map, then quickly located the regions which may contain disaster according to saliency. The recognition and early warning of disaster can be completed, quickly. This method was tested snowstorms and rolling stones are simulated, and gave the corresponding experimental results. Experiment results show the correctness and efficiency of introducing visual attention mechanism into road disaster monitor and early warning system. It is of great significance and practical value for reducing the computation and improving real-time performance of the total system.

Image Reduction Method for Rice Leaf Disease Based on Visual Attention Model

2012 ◽  
Vol 220-223 ◽  
pp. 1393-1397
Author(s):  
Li Bo Liu ◽  
Chun Jiang Zhao ◽  
Hua Rui Wu ◽  
Rong Hua Gao
Keyword(s):  
Visual Attention ◽  
Digital Camera ◽  
Visual Saliency ◽  
Saliency Map ◽  
Brown Spot ◽  
Visual Attention Model ◽  
Attention Model ◽  
Growth Status ◽  
Leaf Disease ◽  
Brown Spot Disease

Analyzing the crop growth status through leaf disease image is one of the hottest issues in agriculture and forestry fields currently. But the size of image gathered by digital camera is too large, the focus of this research is to zooming-out image at the condition of ensuring the main information which carried by the image to distort lower. Based on the further study of visual attention model proposed by Itti and Ma YF. This paper establishes visual attention and visual saliency map of rice blast and brown spot disease image, whose size is 4272*2878 pixels. Finally, determines the reduction scale of the corresponding effective target collection and provide a new way to reduce the plant leaf images.

scholarly journals Gravitational models explain shifts on human visual attention

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Dario Zanca ◽  
Marco Gori ◽  
Stefano Melacci ◽  
Alessandra Rufa
Keyword(s):  
Visual Attention ◽  
Temporal Dynamics ◽  
Sensory Information ◽  
Saliency Map ◽  
Feature Maps ◽  
Single Feature ◽  
Single Location ◽  
Two Phases ◽  
Winner Take All ◽  
Take All

Abstract Visual attention refers to the human brain’s ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It proceeds in two phases. One in which visual feature maps are acquired and processed in parallel. Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning. Its computational description is challenging, especially if the temporal dynamics of the process are taken into account. Numerous methods to estimate saliency have been proposed in the last 3 decades. They achieve almost perfect performance in estimating saliency at the pixel level, but the way they generate shifts in visual attention fully depends on winner-take-all (WTA) circuitry. WTA is implemented by the biological hardware in order to select a location with maximum saliency, towards which to direct overt attention. In this paper we propose a gravitational model to describe the attentional shifts. Every single feature acts as an attractor and the shifts are the result of the joint effects of the attractors. In the current framework, the assumption of a single, centralized saliency map is no longer necessary, though still plausible. Quantitative results on two large image datasets show that this model predicts shifts more accurately than winner-take-all.

scholarly journals Image Modification Based on a Visual Saliency Map for Guiding Visual Attention

2015 ◽  
Vol E98.D (11) ◽  
pp. 1967-1975 ◽  
Author(s):  
Hironori TAKIMOTO ◽  
Tatsuhiko KOKUI ◽  
Hitoshi YAMAUCHI ◽  
Mitsuyoshi KISHIHARA ◽  
Kensuke OKUBO
Keyword(s):  
Visual Attention ◽  
Visual Saliency ◽  
Saliency Map
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