30,443 research outputs found
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
In this paper we propose a novel approach to multi-action recognition that
performs joint segmentation and classification. This approach models each
action using a Gaussian mixture using robust low-dimensional action features.
Segmentation is achieved by performing classification on overlapping temporal
windows, which are then merged to produce the final result. This approach is
considerably less complicated than previous methods which use dynamic
programming or computationally expensive hidden Markov models (HMMs). Initial
experiments on a stitched version of the KTH dataset show that the proposed
approach achieves an accuracy of 78.3%, outperforming a recent HMM-based
approach which obtained 71.2%
Bayesian fusion of hidden Markov models for understanding bimanual movements
Understanding hand and body gestures is a part of a wide spectrum of current research in computer vision and human-computer interaction. A part of this can be the recognition of movements in which the two hands move simultaneously to do something or imply a meaning. We present a Bayesian network for fusing hidden Markov models in order to recognise a bimanual movement. A bimanual movement is tracked and segmented by a tracking algorithm. Hidden Markov models are assigned to the segments in order to learn and recognize the partial movement within each segment. A Bayesian network fuses the HMMs in order to perceive the movement of the two hands as a single entity
Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies
Hyper-spectral data can be analyzed to recover physical properties at large
planetary scales. This involves resolving inverse problems which can be
addressed within machine learning, with the advantage that, once a relationship
between physical parameters and spectra has been established in a data-driven
fashion, the learned relationship can be used to estimate physical parameters
for new hyper-spectral observations. Within this framework, we propose a
spatially-constrained and partially-latent regression method which maps
high-dimensional inputs (hyper-spectral images) onto low-dimensional responses
(physical parameters such as the local chemical composition of the soil). The
proposed regression model comprises two key features. Firstly, it combines a
Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent
response model. While the former makes high-dimensional regression tractable,
the latter enables to deal with physical parameters that cannot be observed or,
more generally, with data contaminated by experimental artifacts that cannot be
explained with noise models. Secondly, spatial constraints are introduced in
the model through a Markov random field (MRF) prior which provides a spatial
structure to the Gaussian-mixture hidden variables. Experiments conducted on a
database composed of remotely sensed observations collected from the Mars
planet by the Mars Express orbiter demonstrate the effectiveness of the
proposed model.Comment: 12 pages, 4 figures, 3 table
A statistical multiresolution approach for face recognition using structural hidden Markov models
This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy
An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition
Traditionally, the performance of ocr algorithms and systems is based on the
recognition of isolated characters. When a system classifies an individual
character, its output is typically a character label or a reject marker that
corresponds to an unrecognized character. By comparing output labels with the
correct labels, the number of correct recognition, substitution errors
misrecognized characters, and rejects unrecognized characters are determined.
Nowadays, although recognition of printed isolated characters is performed with
high accuracy, recognition of handwritten characters still remains an open
problem in the research arena. The ability to identify machine printed
characters in an automated or a semi automated manner has obvious applications
in numerous fields. Since creating an algorithm with a one hundred percent
correct recognition rate is quite probably impossible in our world of noise and
different font styles, it is important to design character recognition
algorithms with these failures in mind so that when mistakes are inevitably
made, they will at least be understandable and predictable to the person
working with theComment: 6pages, 5 figure
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