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Suboptimal eye movements for seeing fine details.
Human eyes are never stable, even during attempts of maintaining gaze on a visual target. Considering transient response characteristics of retinal ganglion cells, a certain amount of motion of the eyes is required to efficiently encode information and to prevent neural adaptation. However, excessive motion of the eyes leads to insufficient exposure to the stimuli, which creates blur and reduces visual acuity. Normal miniature eye movements fall in between these extremes, but it is unclear if they are optimally tuned for seeing fine spatial details. We used a state-of-the-art retinal imaging technique with eye tracking to address this question. We sought to determine the optimal gain (stimulus/eye motion ratio) that corresponds to maximum performance in an orientation-discrimination task performed at the fovea. We found that miniature eye movements are tuned but may not be optimal for seeing fine spatial details
GazeDPM: Early Integration of Gaze Information in Deformable Part Models
An increasing number of works explore collaborative human-computer systems in
which human gaze is used to enhance computer vision systems. For object
detection these efforts were so far restricted to late integration approaches
that have inherent limitations, such as increased precision without increase in
recall. We propose an early integration approach in a deformable part model,
which constitutes a joint formulation over gaze and visual data. We show that
our GazeDPM method improves over the state-of-the-art DPM baseline by 4% and a
recent method for gaze-supported object detection by 3% on the public POET
dataset. Our approach additionally provides introspection of the learnt models,
can reveal salient image structures, and allows us to investigate the interplay
between gaze attracting and repelling areas, the importance of view-specific
models, as well as viewers' personal biases in gaze patterns. We finally study
important practical aspects of our approach, such as the impact of using
saliency maps instead of real fixations, the impact of the number of fixations,
as well as robustness to gaze estimation error
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