50,426 research outputs found
A smart vision sensor for detecting risk factors of a toddler's fall in a home environment
This paper presents a smart vision sensor for detecting risk factors of a toddler's fall in an indoor home environment assisting parents' supervision to prevent fall injuries. We identified the risk factors by analyzing real fall injury stories and referring to a related organization's suggestions to prevent falls. In order to detect the risk factors using computer vision, two major image processing methods, clutter detection and toddler tracking, were studied with using only one commercial web-camera. For practical purposes, there is no need for a toddler to wear any sensors or markers. The algorithms for detection have been developed, implemented and tested
Motion processing deficits in migraine are related to contrast sensitivity
Background: There are conflicting reports concerning the ability of people with migraine to detect and discriminate visual motion. Previous studies used different displays and none adequately assessed other parameters that could affect performance, such as those that could indicate precortical dysfunction.
Methods: Motion-direction detection, discrimination and relative motion thresholds were compared from participants with and without migraine. Potentially relevant visual covariates were included (contrast sensitivity; acuity; stereopsis; visual discomfort, stress, triggers; dyslexia).
Results: For each task, migraine participants were less accurate than a control group and had impaired contrast sensitivity, greater visual discomfort, visual stress and visual triggers. Only contrast sensitivity correlated with performance on each motion task; it also mediated performance.
Conclusions: Impaired performance on certain motion tasks can be attributed to impaired contrast sensitivity early in the visual system rather than a deficit in cortical motion processing per se. There were, however, additional differences for global and relative motion thresholds embedded in noise, suggesting changes in extrastriate cortex in migraine. Tasks to study the effects of noise on performance at different levels of the visual system and across modalities are recommended. A battery of standard visual tests should be included in any future work on the visual system and migraine
Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking
The final publication is available at IOS Press through http://dx.doi.org/10.3233/AIS-170467Peer ReviewedPostprint (author's final draft
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
Real-World Repetition Estimation by Div, Grad and Curl
We consider the problem of estimating repetition in video, such as performing
push-ups, cutting a melon or playing violin. Existing work shows good results
under the assumption of static and stationary periodicity. As realistic video
is rarely perfectly static and stationary, the often preferred Fourier-based
measurements is inapt. Instead, we adopt the wavelet transform to better handle
non-static and non-stationary video dynamics. From the flow field and its
differentials, we derive three fundamental motion types and three motion
continuities of intrinsic periodicity in 3D. On top of this, the 2D perception
of 3D periodicity considers two extreme viewpoints. What follows are 18
fundamental cases of recurrent perception in 2D. In practice, to deal with the
variety of repetitive appearance, our theory implies measuring time-varying
flow and its differentials (gradient, divergence and curl) over segmented
foreground motion. For experiments, we introduce the new QUVA Repetition
dataset, reflecting reality by including non-static and non-stationary videos.
On the task of counting repetitions in video, we obtain favorable results
compared to a deep learning alternative
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