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Zoltan Kato

Bio: Zoltan Kato is an academic researcher from University of Szeged. The author has contributed to research in topics: Image segmentation & Markov random field. The author has an hindex of 23, co-authored 107 publications receiving 1918 citations. Previous affiliations of Zoltan Kato include National University of Singapore & French Institute for Research in Computer Science and Automation.


Papers
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Journal ArticleDOI
TL;DR: A Markov random field image segmentation model, which aims at combining color and texture features through Bayesian estimation via combinatorial optimization (simulated annealing), and a parameter estimation method using the EM algorithm is proposed.

221 citations

Journal ArticleDOI
TL;DR: Three optimisation techniques are presented, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model.

183 citations

Journal ArticleDOI
TL;DR: This paper presents a classical multiscale model which consists of a label pyramid and a whole observation field, and proposes a hierarchical Markov random field model based on this classical model, which results in a relaxation algorithm with a new annealing scheme: the multitemperatureAnnealing (MTA) scheme, which consist of associating higher temperatures to higher levels in order to be less sensitive to local minima at coarser grids.

113 citations

Journal ArticleDOI
TL;DR: This paper proposes parameter estimation methods related to monogrid and hierarchical MRF models that are similar to the expectation–maximization (EM) algorithm and looks at the maximum likelihood estimate of the parameters given a tentative labeling obtained at the previous step.

98 citations

Book
26 Sep 2012
TL;DR: The primary goal of this monograph is to demonstrate the basic steps to construct an easily applicable MRF segmentation model and further develop its multi-scale and hierarchical implementations as well as their combination in a multilayer model.
Abstract: Markov Random Fields in Image Segmentation provides an introduction to the fundamentals of Markovian modeling in image segmentation as well as a brief overview of recent advances in the field. Segmentation is formulated within an image labeling framework, where the problem is reduced to assigning labels to pixels. In a probabilistic approach, label dependencies are modeled by Markov random fields (MRF) and an optimal labeling is determined by Bayesian estimation, in particular maximum a posteriori (MAP) estimation. The main advantage of MRF models is that prior information can be imposed locally through clique potentials. MRF models usually yield a non-convex energy function. The minimization of this function is crucial in order to find the most likely segmentation according to the MRF model. Classical optimization algorithms including simulated annealing and deterministic relaxation are treated along with more recent graph cut-based algorithms. The primary goal of this monograph is to demonstrate the basic steps to construct an easily applicable MRF segmentation model and further develop its multi-scale and hierarchical implementations as well as their combination in a multilayer model. Representative examples from remote sensing and biological imaging are analyzed in full detail to illustrate the applicability of these MRF models. Furthermore, a sample implementation of the most important segmentation algorithms is available as supplementary software. Markov Random Fields in Image Segmentation is an invaluable resource for every student, engineer, or researcher dealing with Markovian modeling for image segmentation.

82 citations


Cited by
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Book
Stan Z. Li1
01 Jan 2001
TL;DR: This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation.
Abstract: Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Discriminative Random Fields (DRF) Strong Random Fields (SRF) Spatial-Temporal Models Total Variation Models Learning MRF for Classification (motivation + DRF) Relation to Graphic Models Graph Cuts Belief Propagation Features: Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images Examines the problems of parameter estimation and function optimization Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.

1,694 citations

Journal ArticleDOI
TL;DR: Baxter has inherited the mantle of Onsager who started the process by solving exactly the two-dimensional Ising model in 1944 as mentioned in this paper, and there has been a growing belief that all the twodimensional lattice statistical models will eventually be solved and that it will be Professor Baxter who solves them.
Abstract: R J Baxter 1982 London: Academic xii + 486 pp price £43.60 Over the past few years there has been a growing belief that all the twodimensional lattice statistical models will eventually be solved and that it will be Professor Baxter who solves them. Baxter has inherited the mantle of Onsager who started the process by solving exactly the two-dimensional Ising model in 1944.

1,658 citations

Book
01 Aug 1995
TL;DR: This book presents a comprehensive study on the use of MRFs for solving computer vision problems, and covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms.
Abstract: From the Publisher: Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition, and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

1,333 citations

Journal ArticleDOI
TL;DR: A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise, and not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching.
Abstract: A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(lm), where l and m are the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 mode random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.

1,230 citations

Journal ArticleDOI
TL;DR: This survey focuses on more generic object categories including, but not limited to, road, building, tree, vehicle, ship, airport, urban-area, and proposes two promising research directions, namely deep learning- based feature representation and weakly supervised learning-based geospatial object detection.
Abstract: Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

994 citations