Yarbus, Eye Movements, and Vision

Tatler, Benjamin W.; Wade, Nicholas; Kwan, H Lee; Findlay, John M.; Velichkovsky, Boris M.

doi:10.1068/i0382

Cited by 173 publications

(123 citation statements)

References 36 publications

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“…His experiments measuring eye movements toward "The Unexpected Visitor" have demonstrated that different tendencies of gaze distributions can be observed according to the given tasks (e.g., free examination, give the ages of the people). The same results have been reported in the tutorial by Tatler et al [18]. Moreover, The tutorial by Hayhoe et al [19] presents some examples that human attention can be oriented to task-relevant locations rather than salient locations in the case humans are given some tasks.…”

Section: Relationship Between Visual Attention Human Intentions Andsupporting

confidence: 71%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation. key words : human visual attention, computational model, saliency, bottom-up, top-down MotivationDeveloping sophisticated algorithms for detecting and recognizing something like objects from a given image and video has been a long distance challenge in pattern recognition and computer vision research fields. In fact, a huge number of studies, techniques and theories related to object detection and recognition have already been developed. In particular, several methods for detecting certain specific categories of objects such as human bodies and human faces have already been put to practical use in for example surveillance, authentication and the human-centric enhancement of image quality, with the best possible use of the prior knowledge of target objects (human bodies and faces) [1], [2]. However, generic object detection and recognition without any constraints as regards the target objects has remained major challenge, because (1) various kinds of objects might constitute the targets and (2) target objects in the same category might have different appearances due to variations of instances in a specific category, illumination changes and so on. † † The author is with the Graduate School of Informatics, Kyoto University, Kyoto-shi, 606-8501 Japan.† † † The author is with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464-8603 Japan.a) E-mail: akisato@ieee.org b) E-mail: yonetani@vision.kuee.kyoto-u.ac.jp c) E-mail: hirayama@is.nagoya-u.ac.jp DOI: 10.1587/transinf.E96.D.562 On the other hand, human beings seem to be able to detect various kinds of objects without any thought or effort. For example, from Fig. 1 left, we can easily and instantly detect a red car, a blue traffic sign and a broad white line. Visual attention [3] is considered to play an important role in achieving this function. Visual attention is one of the built-in mechanisms of the human visual system that quickly selects regions in a visual scene, which are most likely to contain items of interest. Such a pre-selection mechanism focusing only on relevant data would be essential in enabling computers to undertake subsequent processing such as generic o...

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Section: Relationship Between Visual Attention Human Intentions Andsupporting

confidence: 71%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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“…Although his findings were based on only one observer, they have been replicated and extended over the last decade (see DeAngelus & Pelz, 2009; Tatler, Wade, Kwan, Findlay, & Velichovsky, 2010). These studies suggest that eye movement patterns convey information about the task a person was performing while viewing the stimulus.…”

Section: Introductionmentioning

confidence: 88%

What Am I Looking at? Interpreting Dynamic and Static Gaze Displays

2017

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Displays of eye movements may convey information about cognitive processes but require interpretation. We investigated whether participants were able to interpret displays of their own or others' eye movements. In Experiments 1 and 2, participants observed an image under three different viewing instructions. Then they were shown static or dynamic gaze displays and had to judge whether it was their own or someone else's eye movements and what instruction was reflected. Participants were capable of recognizing the instruction reflected in their own and someone else's gaze display. Instruction recognition was better for dynamic displays, and only this condition yielded above chance performance in recognizing the display as one's own or another person's (Experiments 1 and 2). Experiment 3 revealed that order information in the gaze displays facilitated instruction recognition when transitions between fixated regions distinguish one viewing instruction from another. Implications of these findings are discussed.

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“…It is worth noting that methods sharing similarities with iMap have been introduced by various authors before us (e.g., Barrington, Marks, Hsiao, & Cottrell, 2008;Bruce & Tsotsos, 2009;Buchan, Paré, & Munhall, 2007;Harding & Bloj, 2010;Henderson, 2003;Kita et al, 2010;Pomplun, Ritter, & Velichkovsky, 1996;Tatler, Wade, Kwan, Findlay, & Velichkovsky, 2010;Torralba, Oliva, Castelhano, & Henderson, 2006;Wooding, 2002). Here, we briefly present the characteristics that few of these methods share with iMap and what differentiate them from it.…”

mentioning

confidence: 99%

“…Note that using a Gaussian smoothing on fixations, weighted by their durations, is not novel per se, and heat map representations have become very popular in the last few years (see, e.g., Barrington et al, 2008;Bruce & Tsotsos, 2009;Buchan et al, 2007;Harding & Bloj, 2010;Henderson, 2003;Kita et al, 2010;Tatler et al, 2010;Torralba et al, 2006;Watanabe, Matsuda, Nishioka, & Namatame, 2011;Wooding, 2002). However, in most of these cases, the heat maps were used only for illustration purposes.…”

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confidence: 99%

iMap: a novel method for statistical fixation mapping of eye movement data

2011

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Eye movement data analyses are commonly based on the probability of occurrence of saccades and fixations (and their characteristics) in given regions of interest (ROIs). In this article, we introduce an alternative method for computing statistical fixation maps of eye movements-iMap-based on an approach inspired by methods used in functional magnetic resonance imaging. Importantly, iMap does not require the a priori segmentation of the experimental images into ROIs. With iMap, fixation data are first smoothed by convolving Gaussian kernels to generate three-dimensional fixation maps. This procedure embodies eyetracker accuracy, but the Gaussian kernel can also be flexibly set to represent acuity or attentional constraints. In addition, the smoothed fixation data generated by iMap conform to the assumptions of the robust statistical random field theory (RFT) approach, which is applied thereafter to assess significant fixation spots and differences across the three-dimensional fixation maps. The RFT corrects for the multiple statistical comparisons generated by the numerous pixels constituting the digital images. To illustrate the processing steps of iMap, we provide sample analyses of real eye movement data from face, visual scene, and memory processing. The iMap MATLAB toolbox is editable and freely available for download online (www.unifr.ch/psycho/ibmlab/).Keywords Eye movements . Statistical fixation maps . Data-driven analyses . Random field theory . Matlab toolboxThe human visual system is equipped with the most sophisticated machinery to effectively adapt to the visual world. Where, when, and how human eyes are moved to gather information to adapt to the visual environment has been a question that has fascinated scientists for more than a century. Javal (1879) coined the term saccade to describe the rapid movement of the eyes produced during reading, an oculomotor phenomenon identified by Hering (1879) and Lamare (1892) during this period. However, a comprehensive sense of the very nature of those ballistic movements, a description of the use of fixations to gather the information relevant to solving the task at hand, and the scientific definition of saccades came with Dodge (1916) and the development of photographic techniques for recording corneal reflections. This novel recording approach paved the way for the scientific study of eye movements (see Wade, Tatler, & Heller, 2003). Buswell (1935) published the first systematic study on How People Look at Pictures: A Study of The Psychology of Perception in Art. Buswell observed that trained and untrained artists deployed similar fixation patterns to analyze paintings. All observers shared similar oculomotor behavior, deploying initial short fixations over the main features of the paintings, which were subsequently followed by a series of longer fixations. Interestingly, when fixations were collapsed across observers, they highlighted areas containing salient or diagnostic parts of the images. Critically, these observations revealed that eye moveme...

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Yarbus, Eye Movements, and Vision

Cited by 173 publications

References 36 publications

Computational Models of Human Visual Attention and Their Implementations: A Survey

Computational Models of Human Visual Attention and Their Implementations: A Survey

What Am I Looking at? Interpreting Dynamic and Static Gaze Displays

iMap: a novel method for statistical fixation mapping of eye movement data

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