Optimal eye movement strategies in visual search

By Jiri Najemnik, Wilson S. Geisler. Nature, 2005

812 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2207d3952e4823280d297513226c2e075a3ff04c

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Citations in the corpus

Principles of sensorimotor learning (2011, background): “In some laboratory-based visual search tasks it has been shown that the eye movements are at least in qualitative agreement with an optimal extraction of task-relevant informatio”
The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. (2007, background): “Recent accounts have suggested that eye movements may target regions that are maximally informative to the viewer (Najemnik & Geisler, 2005; Raj, Geisler, Frazor, & Bovik, 2005) or that reduce the uncertainty of the object or scene being inspected (Renninger, Vergheese, & Coughlan, 2007).”
Saliency, attention, and visual search: an information theoretic approach. (2009, background): “Najemnik and Geisler (2005) consider ﬁxation behavior predicted by a Bayesian ideal observer with the focus on predicting sequences of ﬁxations.”
What do eyes reveal about the mind?: Algorithmic inference of search targets from fixations (2015, background): “fixations near the end of trial), a systematic investigation of this factor may tell us how subjects accumulate information over time during search (see for example [56]).”
Dynamic visual attention: searching for coding length increments (2008, methodology): “Similar to the computational approximations in [14], we consider the fovea sampling bias as a weighted maskW over the reconstructed saliency map.”
50 Years of object recognition: Directions forward (2013, methodology): “This constitutes an example of a neuromorphic vision system applied to robotics, due to the use of (i) a humanoid robot that emulates human locomotion, (ii) the use of a hierarchical feed-forward recognition system inspired by human vision, and (iii) the use of a next-view planner that shares many o”
A Neural Autoregressive Approach to Attention-based Recognition (2015, background): “Najemnik and Geisler (2005) also investigated what is the optimal eye movement strategy for a foveated visual system dealing with the problem of locating a pre-defined target in a cluttered environment, and whether humans follow optimal eye movement strategies when searching a target.”
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, background): “Najemnik and Geisler [26] showed humans use almost optimal scanning strategies for selecting fixation locations in visual search.”
Independence of luminance and contrast in natural scenes and in the early visual system (2005, methodology): “and simulated scan paths based on the measured statistics of saccade”
Information Content Weighting for Perceptual Image Quality Assessment (2011, background): “To achieve such optimality, the image components that contain more information content would attract more visual attention [33].”
State-of-the-Art in Visual Attention Modeling (2013, neutral): “Some examples include search processes (e.g., optimal search theory [20]), information maximization models [21], Mr. Chips: an ideal-observer model of reading [25], EMMA (Eye Movements and Movement of Attention) model [139], HMM model for controlling eye movements [26], and constrained random walk…”
Navigating Into the Future or Driven by the Past (2013, background): “…of a ball more rapidly and accurately than an amateur’s (Land & McLeod, 2000; see also Abegg, Manoach, & Barton, 2011), but also of spontaneous, self-initiated eye movements optimal for the extraction of information relevant to resolving task-relevant uncertainty (Najemnik & Geisler, 2005).”
Video Quality Assessment Using a Statistical Model of Human Visual Speed Perception (2007, positive): “To achieve such efficiency, it is natural to assume that the areas in the visual scene that contain more information should be more likely to attract visual attention and fixations [17,18].”