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Author

V.N. Vasyukov

Other affiliations: Novosibirsk State University
Bio: V.N. Vasyukov is an academic researcher from Novosibirsk State Technical University. The author has contributed to research in topics: Image segmentation & Random field. The author has an hindex of 3, co-authored 22 publications receiving 31 citations. Previous affiliations of V.N. Vasyukov include Novosibirsk State University.

Papers
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Proceedings ArticleDOI
01 Jun 2016
TL;DR: A new approach to textured image segmentation based on applying finite-valued Hierarchical Markov Random Fields model based on using three-valued Gibbs field for describing textured images is proposed.
Abstract: We propose a new approach to textured image segmentation based on applying finite-valued Hierarchical Markov Random Fields model. The difference of the approach from the formerly known ones is in use of three-valued Gibbs field for describing textured images. We demonstrate the performance of the developed algorithm with examples of modeled and real textured images processing.

3 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: This paper is concerned with Gibbs fields taking on values from finite sets, which allows to overcome difficulties in estimating Gibbs distribution parameters and to synthesize some useful algorithms of image processing.
Abstract: Gibbs (Markov) random fields are used as stochastic picture models in image processing because of their conceptual simplicity and due to the fact that Gibbs models are fit to synthesize algorithms based on Bayes approach. In this paper, we are concerned with Gibbs fields taking on values from finite sets. This restriction allows to overcome difficulties in estimating Gibbs distribution parameters and to synthesize some useful algorithms of image processing.

3 citations

Proceedings Article
06 Jul 2003
TL;DR: For the purpose of increasing the quality of grayscale and color picture reconstruction a new hierarchical two-level Gibbs model using as a lower layer a line process describing edges of regions is suggested, and this algorithm as a by-product extracts edges of picture regions.
Abstract: Gibbs models are being applied in image processing due to easiness of taking into account of pixel interactions. An iteration class of algorithms known as stochastic relaxation is the most powerful tool for solving a great many tasks in image processing. In spite of the fact that many researchers concern themselves with Gibbs model application to image processing, there are problems in this field that are not solved up to date. In this work, authors present some new results obtained recently. In particular new methods of finite-state Gibbs model parameter estimation based on sufficient statistics and on conditional moments, algorithms of texture segmentation, algorithms of grayscale and color picture reconstruction are presented. These algorithms are suitable for reconstructing not only artificially distorted model images, as in most cases of published works, but for reconstructing the images obtained by plain cameras as well. For the purpose of increasing the quality of grayscale and color picture reconstruction a new hierarchical two-level Gibbs model using as a lower layer a line process describing edges of regions is suggested. Experiments give a demonstration of increasing the visually perceptible quality of recovered images. Moreover, this algorithm as a by-product extracts edges of picture regions.

3 citations

Proceedings ArticleDOI
23 Jun 2008
TL;DR: A cellular model for simulating 2D grayscale images of smoke clouds is offered, intended for adjustment of forest fire detection algorithms based on image analysis.
Abstract: A cellular model for simulating 2D grayscale images of smoke clouds is offered. The model is intended for adjustment of forest fire detection algorithms based on image analysis. Simulation of realistic smoke cloud image wellknown to be the fire character, in long image sequence allows estimating detection probability.

2 citations


Cited by
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01 Jan 2009
TL;DR: The study shows that the signal processing methods, such as Gabor filters and wavelets are gaining popularity but old methods are still used but are improved with new calculations or combined with other methods.
Abstract: Texture classification is used in various pattern recognition applications that possess feature-liked appearance. This paper aims to compile the recent trends on the usage of feature extraction and classification methods used in the research of texture classification as well as the texture datasets used for the experiments. The study shows that the signal processing methods, such as Gabor filters and wavelets are gaining popularity but old methods such as GLCM are still used but are improved with new calculations or combined with other methods. For the classifiers, nearest neighbor algorithms are still fairly popular despite being simple and SVM has become a major classifier used in texture classification. For the datasets, Brodatz texture dataset is the most popularly used dataset despite it being old and with limited samples, other datasets are less used.

45 citations

Journal ArticleDOI
01 Feb 2013
TL;DR: This paper proposes a low-cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance, based on a cellular model.
Abstract: An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low-cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. The results show that the proposed approach obtains accurate results.

43 citations

Journal ArticleDOI
TL;DR: In this article, a review summarizes the published data on the widely applied electron-beam, laser-beam as well as resistance upset, projection, and spot welding of zirconium alloys for nuclear applications.

26 citations

Proceedings ArticleDOI
20 Oct 2011
TL;DR: A virtual environment for the creation of synthetic wildfire smoke frame sequences, able to simulate a distant smoke plume and to integrate it with an existing frame sequence is proposed, and results comparable to real situations are shown.
Abstract: In this paper we propose a virtual environment for the creation of synthetic wildfire smoke frame sequences, able to simulate a distant smoke plume and to integrate it with an existing frame sequence. This work provides a virtual tool to measure the accuracy of existing image-based wildfire smoke detection systems without the need to produce real smoke and fires in the environments. The proposed algorithm uses a cellular model driven by the rules of propagation and collision to simulate the basic physical principles of advection, diffusion, buoyancy, and the response to external forces (such as the wind). Adverse environmental conditions like fog and low-light are also simulated, together with the introduction of noise in order to reproduce acquisition defects. The resulting frame sequences are then evaluated by using a smoke detection system, which shows that our method for virtual smoke simulation gives results comparable to real situations. The extracted data can then be used to increase the performance of smoke detection systems when few real data are available.

12 citations

Journal ArticleDOI
TL;DR: The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach methods that have been proposed in the literature is presented.
Abstract: In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in literature and there are a wide variety of approaches that are used. Different approaches are suited to different types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach methods that have been proposed in the literature. The rest of the paper is organized as follows.

10 citations