4,204 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
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
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
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
Change detection in optical aerial images by a multilayer conditional mixed Markov model
In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth
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