9,714 research outputs found
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video:
https://youtu.be/0q6ap_OSBA
Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight
into pathological and physiological alterations of living tissue, with the help
of which researchers hope to predict (local) therapeutic efficacy early and
determine optimal treatment schedule. However, the analysis of qMRI has been
limited to ad-hoc heuristic methods. Our research provides a powerful
statistical framework for image analysis and sheds light on future localized
adaptive treatment regimes tailored to the individual's response. We assume in
an imperfect world we only observe a blurred and noisy version of the
underlying pathological/physiological changes via qMRI, due to measurement
errors or unpredictable influences. We use a hidden Markov random field to
model the spatial dependence in the data and develop a maximum likelihood
approach via the Expectation--Maximization algorithm with stochastic variation.
An important improvement over previous work is the assessment of variability in
parameter estimation, which is the valid basis for statistical inference. More
importantly, we focus on the expected changes rather than image segmentation.
Our research has shown that the approach is powerful in both simulation studies
and on a real dataset, while quite robust in the presence of some model
assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Multi-Scale 3D Scene Flow from Binocular Stereo Sequences
Scene ïŹow methods estimate the three-dimensional motion ïŹeld for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene ïŹow estimation that provides reliable results using only two cameras by fusing stereo and optical ïŹow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical ïŹow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene ïŹow than previous methods allow. To handle the aperture problems inherent in the estimation of optical ïŹow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization â two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108
Hypergraph Modelling for Geometric Model Fitting
In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201
Tracking Cell Signals in Fluorescent Images
In this paper we present the techniques for tracking cell signal in GFP (Green Fluorescent Protein) images of growing cell colonies. We use such tracking for both data extraction and dynamic modeling of intracellular processes. The techniques are based on optimization of energy functions, which simultaneously determines cell correspondences, while estimating the mapping functions. In addition to spatial mappings such as affine and Thin-Plate Spline mapping, the cell growth and cell division histories must be estimated as well. Different levels of joint optimization are discussed. The most unusual tracking feature addressed in this paper is the possibility of one-to-two correspondences caused by cell division. A novel extended softassign algorithm for solutions of one-to-many correspondences is detailed in this paper. The techniques are demonstrated on three sets of data: growing bacillus Subtillus and e-coli colonies and a developing plant shoot apical meristem. The techniques are currently used by biologists for data extraction and hypothesis formation
- âŠ