The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Antti Oulasvirta - One of the best experts on this subject based on the ideXlab platform.
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MobileHCI - Understanding Visual Saliency in Mobile User Interfaces
2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Tuðçe Köroðlu, Niraj Ramesh Dayama, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
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Understanding Visual Saliency in Mobile User Interfaces
22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, 2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Niraj Ramesh Dayama, Tuğçe Köroğlu, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
Luis A. Leiva - One of the best experts on this subject based on the ideXlab platform.
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MobileHCI - Understanding Visual Saliency in Mobile User Interfaces
2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Tuðçe Köroðlu, Niraj Ramesh Dayama, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
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Understanding Visual Saliency in Mobile User Interfaces
22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, 2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Niraj Ramesh Dayama, Tuğçe Köroğlu, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
Dumur T. - One of the best experts on this subject based on the ideXlab platform.
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Movie 6 : Live imaging of nuclei under heat stress
2019Co-Authors: Dumur T.Abstract:Maximum intensity projections of a 30 h time-lapse experiment conducted on the root of an Arabidopsis plant expressing H2B-RFP in wt background during HS treatment. Time from the start of the experiment is indicated in h at the Top Left Corner. Time points were acquired at 30 min intervals
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Movie 8 : Three assembled examples of live imaging of nuclei under heat stress
2019Co-Authors: Dumur T.Abstract:Montage of maximum intensity projections of three 30 h time-lapse experiments conducted on the root of Arabidopsis plants expressing H2B-RFP in wt background during heat stress treatment. Time from the start of the experiment is indicated in h at the Top Left Corner. Time points were acquired at 30 min intervals
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Movie 1 : Live imaging of root growth
2019Co-Authors: Dumur T.Abstract:Time lapse live imaging (15 h) of a growing root from an Arabidopsis plant expressing H2A-RFP, labelling chromatin. The images are composed of multiple fields of view stitched together. The red rectangle represents one field of view. Time from the start of the experiment is indicated in h:min at the Top Left Corner
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Movie 2 : Manual tracking of root growth
2019Co-Authors: Dumur T.Abstract:Maximum intensity projections of a 7 h time-lapse experiment conducted on the root tip of an Arabidopsis plant expressing H2A10-RFP, labelling chromatin. The microscope stage was manually readjusted to follow the same nuclei over time. Time from the start of the experiment is indicated in h:min at the Top Left Corner. Time points were acquired at 10 min intervals. Post-acquisition registration was made with the “Correct 3D drift” plugin in Fiji
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Movie 5 : Automatic tracking of nuclei
2019Co-Authors: Dumur T.Abstract:Maximum intensity projections of a 4.5 h time-lapse experiment conducted on the elongation zone of an Arabidopsis root expressing H2B-RFP, labelling chromatin. The microscope stage was readjusted to follow the same nuclei over time using the second version of TARDIS software. Time from the start of the experiment is indicated in h:min at the Top Left Corner. Time points were acquired at 10 min intervals. Post-acquisition registration was made with the “Correct 3D drift” plugin in Fiji
Hamed R. Tavakoli - One of the best experts on this subject based on the ideXlab platform.
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MobileHCI - Understanding Visual Saliency in Mobile User Interfaces
2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Tuðçe Köroðlu, Niraj Ramesh Dayama, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
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Understanding Visual Saliency in Mobile User Interfaces
22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, 2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Niraj Ramesh Dayama, Tuğçe Köroğlu, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
Niraj Ramesh Dayama - One of the best experts on this subject based on the ideXlab platform.
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MobileHCI - Understanding Visual Saliency in Mobile User Interfaces
2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Tuðçe Köroðlu, Niraj Ramesh Dayama, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
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Understanding Visual Saliency in Mobile User Interfaces
22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, 2020Co-Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Niraj Ramesh Dayama, Tuğçe Köroğlu, Antti OulasvirtaAbstract:For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on deskTop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the Top-Left Corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.