1,155 research outputs found
An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
In statistical model based texture feature extraction, features based on spatially varying parameters achievehigher discriminative performances compared to spatially constant parameters. In this paper we formulate anovel Bayesian framework which achieves texture characterization by spatially varying parameters based onGaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm.The distributions of estimated spatially varying parameters are then used as successful discriminant texturefeatures in classification and segmentation. Results show that novel features outperform traditional GaussianMarkov random field texture features which use spatially constant parameters. These features capture bothpixel spatial dependencies and structural properties of a texture giving improved texture features for effectivetexture classification and segmentation
Revisiting Boltzmann learning: parameter estimation in Markov random fields
This contribution concerns a generalization of the Boltzmann Machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann Machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov Field
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
Spatially adaptive Bayesian image reconstruction through locally-modulated Markov random field models
The use of Markov random field (MRF) models has proven to be a fruitful approach in a wide range of image processing applications. It allows local texture information to be incorporated in a systematic and unified way and allows statistical inference theory to be applied giving rise to novel output summaries and enhanced image interpretation. A great advantage of such low-level approaches is that they lead to flexible models, which can be applied to a wide range of imaging problems without the need for significant modification.
This paper proposes and explores the use of conditional MRF models for situations where multiple images are to be processed simultaneously, or where only a single image is to be reconstructed and a sequential approach is taken. Although the coupling of image intensity values is a special case of our approach, the main extension over previous proposals is to allow the direct coupling of other properties, such as smoothness or texture. This is achieved using a local modulating function which adjusts the influence of global smoothing without the need for a fully inhomogeneous prior model. Several modulating functions are considered and a detailed simulation study, motivated by remote sensing applications in archaeological geophysics, of conditional reconstruction is presented. The results demonstrate that a substantial improvement in the quality of the image reconstruction, in terms of errors and residuals, can be achieved using this approach, especially at locations with rapid changes in the underlying intensity
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Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation
Image segmentation is a fundamental process in many image, video, and computer
vision applications. Object extraction and object recognition are typical applications
that use segmentation as a low level image processing. Most of the existing color image
segmentation approaches, define a region based on color similarity. This assumption
often makes it difficult for many algorithms to separate the objects of interest which
consist of highlights, shadows and shading which causes inhomogeneous colors of the
objects’ surface.
Bayesian classification and decision making are based on probability theory and
choosing the most probable or the lowest risk. A useful property of the statistical
classifier like Bayesian is that, it is optimal in the sense that it minimizes the expected
mis classification rate. However when the number of features increased, Bayesian
classifier is quite expensive both in terms of computational time and memory. This
thesis proposes a Bayesian color segmentation method which is robust and simple for
real time color segmentation even in presence of environmental light effect. In this
study a decision boundary equation, which is acquired from class conditional probability density function (PDF) of colors, based on Bayes decision theory has been
used for desired color segmentation. The estimation of unknown PDF is a common
problem and in this study Gaussian kernel function which is most widely used
nonparametric density estimation method has been used for PDF calculation.
Comparisons were made between the proposed method to the k-nearest neighbor
(KNN) and support vector machine (SVM), methods for image segmentation.
Experimental results show that the proposed algorithm works better than other two
methods in terms of classifier accuracy with result of more than 99 percent successful
segmentation of desired color in varying illumination. In order to show the real time
ability and robustness of proposed method for color segmentation, experimental results
conducted on vision based mobile robot for navigation. First the robot was trained by
some training sample of desired target color in environment. The decision boundary
which acquired in the teaching phase has been used for real time color segmentation as
the robot move in the environment. Spatial information of desired color in segmented
image has been used for calculating the robot heading angle which is used by mobile
robot controller for navigation.
However, all of the existing color image segmentation approaches are strongly
application dependent. This study shows that proposed algorithm successfully cope with
the varying illumination which causes uneven colors of the objects’ surface. The
experimental results show the proposed algorithm is simple and robust, for real time
application on vision based mobile robot for navigation, in spite of presence of other
shapes and colors in the environmen
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