20,461 research outputs found
Attention Allocation Aid for Visual Search
This paper outlines the development and testing of a novel, feedback-enabled
attention allocation aid (AAAD), which uses real-time physiological data to
improve human performance in a realistic sequential visual search task. Indeed,
by optimizing over search duration, the aid improves efficiency, while
preserving decision accuracy, as the operator identifies and classifies targets
within simulated aerial imagery. Specifically, using experimental eye-tracking
data and measurements about target detectability across the human visual field,
we develop functional models of detection accuracy as a function of search
time, number of eye movements, scan path, and image clutter. These models are
then used by the AAAD in conjunction with real time eye position data to make
probabilistic estimations of attained search accuracy and to recommend that the
observer either move on to the next image or continue exploring the present
image. An experimental evaluation in a scenario motivated from human
supervisory control in surveillance missions confirms the benefits of the AAAD.Comment: To be presented at the ACM CHI conference in Denver, Colorado in May
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Finding any Waldo: zero-shot invariant and efficient visual search
Searching for a target object in a cluttered scene constitutes a fundamental
challenge in daily vision. Visual search must be selective enough to
discriminate the target from distractors, invariant to changes in the
appearance of the target, efficient to avoid exhaustive exploration of the
image, and must generalize to locate novel target objects with zero-shot
training. Previous work has focused on searching for perfect matches of a
target after extensive category-specific training. Here we show for the first
time that humans can efficiently and invariantly search for natural objects in
complex scenes. To gain insight into the mechanisms that guide visual search,
we propose a biologically inspired computational model that can locate targets
without exhaustive sampling and generalize to novel objects. The model provides
an approximation to the mechanisms integrating bottom-up and top-down signals
during search in natural scenes.Comment: Number of figures: 6 Number of supplementary figures: 1
Eye movement patterns during the recognition of three-dimensional objects: Preferential fixation of concave surface curvature minima
This study used eye movement patterns to examine how high-level shape information is used during 3D object recognition. Eye movements were recorded while observers either actively memorized or passively viewed sets of novel objects, and then during a subsequent recognition memory task. Fixation data were contrasted against different algorithmically generated models of shape analysis based on: (1) regions of internal concave or (2) convex surface curvature discontinuity or (3) external bounding contour. The results showed a preference for fixation at regions of internal local features during both active memorization and passive viewing but also for regions of concave surface curvature during the recognition task. These findings provide new evidence supporting the special functional status of local concave discontinuities in recognition and show how studies of eye movement patterns can elucidate shape information processing in human vision
Effects of Alzheimer’s Disease on Visual Target Detection: A “Peripheral Bias”
Visual exploration is an omnipresent activity in everyday life, and might represent an important determinant of visual attention deficits in patients with Alzheimer’s Disease (AD). The present study aimed at investigating visual search performance in AD patients, in particular target detection in the far periphery, in daily living scenes. Eighteen AD patients and 20 healthy controls participated in the study. They were asked to freely explore a hemispherical screen, covering ±90°, and to respond to targets presented at 10°, 30°, and 50° eccentricity, while their eye movements were recorded. Compared to healthy controls, AD patients recognized less targets appearing in the center. No difference was found in target detection in the periphery. This pattern was confirmed by the fixation distribution analysis. These results show a neglect for the central part of the visual field for AD patients and provide new insights by mean of a search task involving a larger field of view
Object Detection Through Exploration With A Foveated Visual Field
We present a foveated object detector (FOD) as a biologically-inspired
alternative to the sliding window (SW) approach which is the dominant method of
search in computer vision object detection. Similar to the human visual system,
the FOD has higher resolution at the fovea and lower resolution at the visual
periphery. Consequently, more computational resources are allocated at the
fovea and relatively fewer at the periphery. The FOD processes the entire
scene, uses retino-specific object detection classifiers to guide eye
movements, aligns its fovea with regions of interest in the input image and
integrates observations across multiple fixations. Our approach combines modern
object detectors from computer vision with a recent model of peripheral pooling
regions found at the V1 layer of the human visual system. We assessed various
eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD
performs on par with the SW detector while bringing significant computational
cost savings.Comment: An extended version of this manuscript was published in PLOS
Computational Biology (October 2017) at
https://doi.org/10.1371/journal.pcbi.100574
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