653 research outputs found
Markov mezĆk a kĂ©pmodellezĂ©sben, alkalmazĂĄsuk az automatikus kĂ©pszegmentĂĄlĂĄs terĂŒletĂ©n = Markovian Image Models: Applications in Unsupervised Image Segmentation
1) KifejlesztettĂŒnk egy olyan szĂn Ă©s textĂșra alapĂș szegmentĂĄlĂł MRF algoritmust, amely alkalmas egy kĂ©p automatikus szegmentĂĄlĂĄsĂĄt elvĂ©gezni. Az eredmĂ©nyeinket az Image and Vision Computing folyĂłiratban publikĂĄltuk. 2) KifejlesztettĂŒnk egy Reversible Jump Markov Chain Monte Carlo technikĂĄn alapulĂł automatikus kĂ©pszegmentĂĄlĂł eljĂĄrĂĄst, melyet sikeresen alkalmaztunk szĂnes kĂ©pek teljesen automatikus szegmentĂĄlĂĄsĂĄra. Az eredmĂ©nyeinket a BMVC 2004 konferenciĂĄn Ă©s az Image and Vision Computing folyĂłiratban publikĂĄltuk. 3) A modell többrĂ©tegƱ tovĂĄbbfejlesztĂ©sĂ©t alkalmaztuk video objektumok szĂn Ă©s mozgĂĄs alapĂș szegmentĂĄlĂĄsĂĄra, melynek eredmĂ©nyeit a HACIPPR 2005 illetve az ACCV 2006 nemzetközi konferenciĂĄkon publikĂĄltuk. SzintĂ©n ehhez az alapproblĂ©mĂĄhoz kapcsolĂłdik HorvĂĄth PĂ©ter hallgatĂłmmal az optic flow szamĂtĂĄsĂĄval illetve szĂn, textĂșra Ă©s mozgĂĄs alapĂș GVF aktĂv kontĂșrral kapcsoltos munkĂĄink. TDK dolgozata elsĆ helyezĂ©st Ă©rt el a 2004-es helyi versenyen, az eredmĂ©nyeinket pedig a KEPAF 2004 konferenciĂĄn publikĂĄltuk. 4) HorvĂĄth PĂ©ter PhD hallgatĂłmmal illetve az franciaorszĂĄgi INRIA Ariana csoportjĂĄval, kidolgoztunk egy olyan kĂ©pszegmentĂĄlĂł eljĂĄrĂĄst, amely a szegmentĂĄlandĂł objektum alakjĂĄt is figyelembe veszi. Az eredmĂ©nyeinket az ICPR 2006 illetve az ICCVGIP 2006 konferenciĂĄn foglaltuk össze. A modell elĆzmĂ©nyekĂ©nt kidolgoztunk tovĂĄbbĂĄ egy alakzat-momemntumokon alapulĂł aktĂv kontĂșr modellt, amelyet a HACIPPR 2005 konferenciĂĄn publikĂĄltunk. | 1) We have proposed a monogrid MRF model which is able to combine color and texture features in order to improve the quality of segmentation results. We have also solved the estimation of model parameters. This work has been published in the Image and Vision Computing journal. 2) We have proposed an RJMCMC sampling method which is able to identify multi-dimensional Gaussian mixtures. Using this technique, we have developed a fully automatic color image segmentation algorithm. Our results have been published at BMVC 2004 international conference and in the Image and Vision Computing journal. 3) A new multilayer MRF model has been proposed which is able to segment an image based on multiple cues (such as color, texture, or motion). This work has been published at HACIPPR 2005 and ACCV 2006 international conferences. The work on optic flow computation and color-, texture-, and motion-based GVF active contours doen with my student, Mr. Peter Horvath, won a first price at the local Student Research Competition in 2004. Results have been presented at KEPAF 2004 conference. 4) A new shape prior, called 'gas of circles' has been introduced using active contour models. This work is done in collaboration with the Ariana group of INRIA, France and my PhD student, Mr. Peter Horvath. Results are published at the ICPR 2006 and ICCVGIP 2006 conferences. A preliminary study on active contour models using shape-moments has also been done, these results are published at HACIPPR 2005
Adaptive segmentation of textured images by using the coupled Markov random field model
Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Model-based learning of local image features for unsupervised texture segmentation
Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of images. Typically, this requires a large set of training images with similar
textures and ground truth segmentation. In this work, we propose a framework to
learn features for texture segmentation when no such training data is
available. The cost function for our learning process is constructed to match a
commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an
approximately piecewise constant feature image with a small jump set. Based on
this idea, we develop a two-stage algorithm which first learns suitable
convolutional features and then performs a segmentation. We note that the
features can be learned from a small set of images, from a single image, or
even from image patches. The proposed method achieves a competitive rank in the
Prague texture segmentation benchmark, and it is effective for segmenting
histological images
Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images
In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of ground truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches
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
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