35,689 research outputs found
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Parametric Inference for Biological Sequence Analysis
One of the major successes in computational biology has been the unification,
using the graphical model formalism, of a multitude of algorithms for
annotating and comparing biological sequences. Graphical models that have been
applied towards these problems include hidden Markov models for annotation,
tree models for phylogenetics, and pair hidden Markov models for alignment. A
single algorithm, the sum-product algorithm, solves many of the inference
problems associated with different statistical models. This paper introduces
the \emph{polytope propagation algorithm} for computing the Newton polytope of
an observation from a graphical model. This algorithm is a geometric version of
the sum-product algorithm and is used to analyze the parametric behavior of
maximum a posteriori inference calculations for graphical models.Comment: 15 pages, 4 figures. See also companion paper "Tropical Geometry of
Statistical Models" (q-bio.QM/0311009
Maximum a Posteriori Estimation by Search in Probabilistic Programs
We introduce an approximate search algorithm for fast maximum a posteriori
probability estimation in probabilistic programs, which we call Bayesian ascent
Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with
varying number of mutually dependent finite, countable, and continuous random
variables. BaMC is an anytime MAP search algorithm applicable to any
combination of random variables and dependencies. We compare BaMC to other MAP
estimation algorithms and show that BaMC is faster and more robust on a range
of probabilistic models.Comment: To appear in proceedings of SOCS1
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
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