1,871 research outputs found
Image segmentation and feature extraction for recognizing strokes in tennis game videos
This paper addresses the problem of recognizing human actions from video. Particularly, the case of recognizing events in tennis game videos is analyzed. Driven by our domain knowledge, a robust player segmentation algorithm is developed real video data. Further, we introduce a number of novel features to be extracted for our particular application. Different feature combinations are investigated in order to find the optimal one. Finally, recognition results for different classes of tennis strokes using automatic learning capability of Hidden Markov Models (HMMs) are presented. The experimental results demonstrate that our method is close to realizing statistics of tennis games automatically using ordinary TV broadcast videos
Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra
The decreasing costs and increasing speed and accuracy of DNA sample
collection, preparation, and sequencing has rapidly produced an enormous volume
of genetic data. However, fast and accurate analysis of the samples remains a
bottleneck. Here we present DRAGenS, a genetic sequence identification
algorithm that exhibits the Big Data handling and computational power of the
Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear
algebra and statistical properties to increase computational performance while
retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield
speed and precision tradeoffs, with applications in biodefense and medical
diagnostics. The DRAGenS analysis algorithm is tested over several
datasets, including three utilized for the Defense Threat Reduction Agency
(DTRA) metagenomic algorithm contest
Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication
In this paper, we present a novel algorithm for the maximum a posteriori
decoding (MAPD) of time-homogeneous Hidden Markov Models (HMM), improving the
worst-case running time of the classical Viterbi algorithm by a logarithmic
factor. In our approach, we interpret the Viterbi algorithm as a repeated
computation of matrix-vector -multiplications. On time-homogeneous
HMMs, this computation is online: a matrix, known in advance, has to be
multiplied with several vectors revealed one at a time. Our main contribution
is an algorithm solving this version of matrix-vector -multiplication
in subquadratic time, by performing a polynomial preprocessing of the matrix.
Employing this fast multiplication algorithm, we solve the MAPD problem in
time for any time-homogeneous HMM of size and observation
sequence of length , with an extra polynomial preprocessing cost negligible
for . To the best of our knowledge, this is the first algorithm for the
MAPD problem requiring subquadratic time per observation, under the only
assumption -- usually verified in practice -- that the transition probability
matrix does not change with time.Comment: AAAI 2016, to appea
Likelihood-based inference of B-cell clonal families
The human immune system depends on a highly diverse collection of
antibody-making B cells. B cell receptor sequence diversity is generated by a
random recombination process called "rearrangement" forming progenitor B cells,
then a Darwinian process of lineage diversification and selection called
"affinity maturation." The resulting receptors can be sequenced in high
throughput for research and diagnostics. Such a collection of sequences
contains a mixture of various lineages, each of which may be quite numerous, or
may consist of only a single member. As a step to understanding the process and
result of this diversification, one may wish to reconstruct lineage membership,
i.e. to cluster sampled sequences according to which came from the same
rearrangement events. We call this clustering problem "clonal family
inference." In this paper we describe and validate a likelihood-based framework
for clonal family inference based on a multi-hidden Markov Model (multi-HMM)
framework for B cell receptor sequences. We describe an agglomerative algorithm
to find a maximum likelihood clustering, two approximate algorithms with
various trade-offs of speed versus accuracy, and a third, fast algorithm for
finding specific lineages. We show that under simulation these algorithms
greatly improve upon existing clonal family inference methods, and that they
also give significantly different clusters than previous methods when applied
to two real data sets
A Unifying review of linear gaussian models
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model.We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models
Hidden Markov Models
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research
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