21,200 research outputs found
Key-Pose Prediction in Cyclic Human Motion
In this paper we study the problem of estimating innercyclic time intervals
within repetitive motion sequences of top-class swimmers in a swimming channel.
Interval limits are given by temporal occurrences of key-poses, i.e.
distinctive postures of the body. A key-pose is defined by means of only one or
two specific features of the complete posture. It is often difficult to detect
such subtle features directly. We therefore propose the following method: Given
that we observe the swimmer from the side, we build a pictorial structure of
poselets to robustly identify random support poses within the regular motion of
a swimmer. We formulate a maximum likelihood model which predicts a key-pose
given the occurrences of multiple support poses within one stroke. The maximum
likelihood can be extended with prior knowledge about the temporal location of
a key-pose in order to improve the prediction recall. We experimentally show
that our models reliably and robustly detect key-poses with a high precision
and that their performance can be improved by extending the framework with
additional camera views.Comment: Accepted at WACV 2015, 8 pages, 3 figure
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
This paper presents a novel method for ground segmentation in Velodyne point
clouds. We propose an encoding of sparse 3D data from the Velodyne sensor
suitable for training a convolutional neural network (CNN). This general
purpose approach is used for segmentation of the sparse point cloud into ground
and non-ground points. The LiDAR data are represented as a multi-channel 2D
signal where the horizontal axis corresponds to the rotation angle and the
vertical axis the indexes channels (i.e. laser beams). Multiple topologies of
relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and
evaluated using a manually annotated dataset we prepared. The results show
significant improvement of performance over the state-of-the-art method by
Zhang et al. in terms of speed and also minor improvements in terms of
accuracy.Comment: ICRA 2018 submissio
An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation
An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.National Science Foundation (IIS-9912573, EIA-9809340
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Camera Calibration from Dynamic Silhouettes Using Motion Barcodes
Computing the epipolar geometry between cameras with very different
viewpoints is often problematic as matching points are hard to find. In these
cases, it has been proposed to use information from dynamic objects in the
scene for suggesting point and line correspondences.
We propose a speed up of about two orders of magnitude, as well as an
increase in robustness and accuracy, to methods computing epipolar geometry
from dynamic silhouettes. This improvement is based on a new temporal
signature: motion barcode for lines. Motion barcode is a binary temporal
sequence for lines, indicating for each frame the existence of at least one
foreground pixel on that line. The motion barcodes of two corresponding
epipolar lines are very similar, so the search for corresponding epipolar lines
can be limited only to lines having similar barcodes. The use of motion
barcodes leads to increased speed, accuracy, and robustness in computing the
epipolar geometry.Comment: Update metadat
Contextual cropping and scaling of TV productions
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
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