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      Eye movements during change detection: the role of depth of field

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          This study investigates the eye movements when detecting changes in the scene with different levels of depth of field. A within-subjects experiment was conducted using a flicker paradigm to create change blindness phenomenon. This experiment investigated two main factors: depth of field and position. The eye-tracker Tobii X120 was used to record participants’ eye movements when looking for changes in the flickering scenes. It was concluded that a small depth of field could indeed direct viewers’ attention into the sharp area. The size of the depth of field could not influence the amount of time for change detection whereas uniform blur could facilitate change detection.

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          Most cited references 17

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          Eye fixations and cognitive processes

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            A new sense for depth of field.

            This paper examines a novel source of depth information: focal gradients resulting from the limited depth of field inherent in most optical systems. Previously, autofocus schemes have used depth of field to measured depth by searching for the lens setting that gives the best focus, repeating this search separately for each image point. This search is unnecessary, for there is a smooth gradient of focus as a function of depth. By measuring the amount of defocus, therefore, we can estimate depth simultaneously at all points, using only one or two images. It is proved that this source of information can be used to make reliable depth maps of useful accuracy with relatively minimal computation. Experiments with realistic imagery show that measurement of these optical gradients can provide depth information roughly comparable to stereo disparity or motion parallax, while avoiding image-to-image matching problems.
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              Failure to detect displacement of the visual world during saccadic eye movements


                Author and article information

                Cognitive Computation and Systems
                Cogn. Comput. Syst.
                The Institution of Engineering and Technology
                16 May 2019
                19 June 2019
                July 2019
                : 1
                : 2
                : 55-59
                [1 ] Jiangsu Key Laboratory of Special Robot Technology, College of Internet of Things Engineering, Hohai University , No. 200 Jingling North Road, Changzhou, People's Republic of China
                [2 ] College of Mechanical and Electrical Engineering, Hohai University , People's Republic of China
                CCS.2019.0003 CCS.2019.0003

                This is an open access article published by the IET in partnership with Shenzhen University under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                Page count
                Pages: 0
                Funded by: National Nature Science Foundation of China
                Award ID: No. 61603123, No. 71601068, No. 61703140
                Funded by: Key research and development program of Jiangsu
                Award ID: BE2017071, BE2017647
                Funded by: Project of international cooperation and exchanges of Changzhou
                Award ID: CZ20170018
                Funded by: Changzhou Technical Key Project
                Award ID: CE20175032
                Research Article


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