111,845 research outputs found
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
We present an automatic method to describe clinically useful information
about scanning, and to guide image interpretation in ultrasound (US) videos of
the fetal heart. Our method is able to jointly predict the visibility, viewing
plane, location and orientation of the fetal heart at the frame level. The
contributions of the paper are three-fold: (i) a convolutional neural network
architecture is developed for a multi-task prediction, which is computed by
sliding a 3x3 window spatially through convolutional maps. (ii) an anchor
mechanism and Intersection over Union (IoU) loss are applied for improving
localization accuracy. (iii) a recurrent architecture is designed to
recursively compute regional convolutional features temporally over sequential
frames, allowing each prediction to be conditioned on the whole video. This
results in a spatial-temporal model that precisely describes detailed heart
parameters in challenging US videos. We report results on a real-world clinical
dataset, where our method achieves performance on par with expert annotations.Comment: To appear in MICCAI, 201
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to deformation. Models based on colour statistics have
complementary traits: they cope well with variation in shape, but suffer when
illumination is not consistent throughout a sequence. Moreover, colour
distributions alone can be insufficiently discriminative. In this paper, we
show that a simple tracker combining complementary cues in a ridge regression
framework can operate faster than 80 FPS and outperform not only all entries in
the popular VOT14 competition, but also recent and far more sophisticated
trackers according to multiple benchmarks.Comment: To appear in CVPR 201
Correlation Filters with Limited Boundaries
Correlation filters take advantage of specific properties in the Fourier
domain allowing them to be estimated efficiently: O(NDlogD) in the frequency
domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the
number of signals. Recent extensions to correlation filters, such as MOSSE,
have reignited interest of their use in the vision community due to their
robustness and attractive computational properties. In this paper we
demonstrate, however, that this computational efficiency comes at a cost.
Specifically, we demonstrate that only 1/D proportion of shifted examples are
unaffected by boundary effects which has a dramatic effect on
detection/tracking performance. In this paper, we propose a novel approach to
correlation filter estimation that: (i) takes advantage of inherent
computational redundancies in the frequency domain, and (ii) dramatically
reduces boundary effects. Impressive object tracking and detection results are
presented in terms of both accuracy and computational efficiency.Comment: 8 pages, 6 figures, 2 table
Circle-based Eye Center Localization (CECL)
We propose an improved eye center localization method based on the Hough
transform, called Circle-based Eye Center Localization (CECL) that is simple,
robust, and achieves accuracy on a par with typically more complex
state-of-the-art methods. The CECL method relies on color and shape cues that
distinguish the iris from other facial structures. The accuracy of the CECL
method is demonstrated through a comparison with 15 state-of-the-art eye center
localization methods against five error thresholds, as reported in the
literature. The CECL method achieved an accuracy of 80.8% to 99.4% and ranked
first for 2 of the 5 thresholds. It is concluded that the CECL method offers an
attractive alternative to existing methods for automatic eye center
localization.Comment: Published and presented at The 14th IAPR International Conference on
Machine Vision Applications, 2015. http://www.mva-org.jp/mva2015
A method for delineation of bone surfaces in photoacoustic computed tomography of the finger
Photoacoustic imaging of interphalangeal peripheral joints is of interest in
the context of using the synovial membrane as a surrogate marker of rheumatoid
arthritis. Previous work has shown that ultrasound produced by absorption of
light at the epidermis reflects on the bone surfaces within the finger. When
the reflected signals are backprojected in the region of interest, artifacts
are produced, confounding interpretation of the images. In this work, we
present an approach where the photoacoustic signals known to originate from the
epidermis, are treated as virtual ultrasound transmitters, and a separate
reconstruction is performed as in ultrasound reflection imaging. This allows us
to identify the bone surfaces. Further, the identification of the joint space
is important as this provides a landmark to localize a region-of-interest in
seeking the inflamed synovial membrane. The ability to delineate bone surfaces
allows us not only to identify the artifacts, but also to identify the
interphalangeal joint space without recourse to new US hardware or a new
measurement. We test the approach on phantoms and on a healthy human finger
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