Pattern Recognition and Image Analysis (Pattern Recogn Image Anal)

Publisher: Nauchnyĭ sovet po kompleksnoĭ probleme "Kibernetika" (Akademii︠a︡ nauk SSSR), MAIK Nauka/Interperiodica

Journal description

Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications is an international journal featuring top papers in pattern recognition, image recognition, analysis, understanding, and processing. The Editorial Board is headed by Yuri Zhuravlev, a prominent Russian mathematician, Full Member of the Russian Academy of Sciences. The board also includes distinguished scientists and engineers from the Russian Academy of Sciences, CIS universities and industry, as well as internationally recognized experts in the field from the USA and Europe. The authors are experts in research and applications. Emphasis is made on rapid publishing of concise articles covering theory, methodology, and practical applications. Major topics include mathematical theory of pattern recognition, raw data representation, computer vision, image processing, machine learning, computer graphics, data and knowledge bases, neural nets, software, specialized computer architectures, applications, and related areas.

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website Pattern Recognition and Image Analysis website
Other titles Raspoznavanie obrazov i analiz izobrazheniĭ
ISSN 1054-6618
OCLC 60627519
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

MAIK Nauka/Interperiodica

  • Pre-print
    • Archiving status unclear
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • On author or institutional server only
    • On a non-profit server
    • Publisher copyright and source must be acknowledged
    • Must link to publisher's website
  • Classification
    blue

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: The methods and algorithms for evaluating geometrical parameters of 2D and 3D tree structures are represented. Main areas of applying methods are biomedical problems connected with analyzing and measuring features of vascular system, i.e., a retina (a 2D structure) and a cardiovascular system (3D structure)). An experimental investigation has been carried out of the stability of the evaluation methods against noises of different natures and the possibility of clustering of selection of vessels based on these features.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: This article considers the problem of constructing a sequential computational procedure for detecting artificial changes of remote sensing data (RSD) using a set of elementary algorithms of detecting artificial RSD changes. The stated task has been solved within the framework of the passive approach, which requires determining actual changes (forgeries) in RSD based on computer analysis.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: The objective of this work was to assess the transport accessibility of agricultural lands in the central and southern districts of Krasnoyarsk krai and Khakassia. An assessment technique based on using geospatial data is presented. The assessment was made based on the use of the Dijkstra’s algorithm for searching state space and applying the model of obstacles that are specific to agrarian and agricultural landscapes in the given areas.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: This paper describes the system for identifying human emotional reactions. Particular attention is devoted to the motion and gesture recognition approaches and methods. The existing systems for recognizing human emotion are briefly reviewed. The scopes of the developed system and current state of the project are described.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: In many fields of modern science, there are problems adequate formalization of which is indispensable for obtaining practically and theoretically important results. In the terminology of the scientific school of academician Yu.I. Zhuravlev, a formalized problem is uniquely defined by the matrix of information and the information matrix. In the present paper, a whole class of issues related to the formalization of recognition/classification problems is considered, and a universal formalism is proposed for carrying out a metric analysis of poorly formalized problems. Thus, the formalization of a problem can be represented as a successive transition from the set of original descriptions to a particular topology, then to a lattice, and then to a certain metric space. It is shown that the property of Zhuravlev’s regularity is sufficient for the existence of bijective mappings between these mathematical constructs. The possibilities of application of the apparatus developed are illustrated by several issues important for the formalization of the problems: introduction of metrics on the sets of the features and metrics on the sets of objects and analysis of “interactions” between dissimilar feature descriptions.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: Can we see the multidimensional world with our own eyes? The answer is yes! We can already see the colorful four-dimensional world made by man. When operations with a multidimensional cube are discussed at seminars on discrete mathematics, the geometric perception of all that is beyond the usual threedimensional space is believed to be impossible. In this paper, we try to dispel this illusion, which got a foothold in our minds. To do this, we need only the elements of projective geometry, but with projection on the threedimensional space.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: This work considers how the research vision behavior model is used to analyze medical images. An viewing trajectory was formed for each image; the distribution of the frequencies of its angular parameters and the distribution of the considered fields were estimated. The correlation coefficient for images of the same class varied within 0.62–0.89, whereas the correlation coefficient in comparing images of different classes was less than 0.3.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: In this paper, we propose a fast and novel detection method based on several samples to localize objects in target images or video. Firstly, we use several samples to train a voting space which is constructed by cells at corresponding positions. Each cell is described by a Gaussian distribution whose parameters are estimated by maximum likelihood estimation method. Then, we randomly choose one sample as a query image. Patches of target image are recognized by densely voting in the trained voting space. Next, we use a mean-shift method to refine multiple instances of object class. The high performance of our approach is demonstrated on several challenging data sets in both efficiency and effectiveness.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: Problems of composing a feature description and developing a comparison procedure for the images of paintings in attribution are considered. A feature description of a facture of paintings based on the characteristics of a grayscale image relief and elements of the structure tensor is proposed. In contrast to known techniques, the feature description is formed only by informative fragments of images and does not require preliminary segmentation of individual brushstrokes. Parameters of feature extraction procedures are chosen. Measures of dissimilarity between images of paintings are proposed. Computational experiments are carried out. The feature description proposed is a quantitative characteristic of the artistic style of an author. The procedure developed for comparing images can be applied together with other types of investigation of paintings to make an attributional conclusion.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: This article considers a method of improving print quality for screenshots. The proposed method is based on detecting and vectorizing text areas on raster images. The main study is dedicated to smooth screen-text segmentation, determining background and text color, improving resolution, and recovering the contour of symbols and approximating them with Bezier curves. The proposed method is resistant to different colors, text sizes, and languages and makes it possible to obtain a sharp and correct text display for printing.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: The algorithm for detecting cars in color images obtained by aerophotography has been presented. The approach is as follows: the majority of cars are painted in one color and have similar sizes. This makes possible to separate cars in the background as areas with color characteristics that differ from the background and have a certain size and form. The areas are generated by integrating hierarchically the smaller areas according to similarity of color and space characteristics. The algorithm is tested on a set of images containing 2226 cars in all.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: An algorithm has been suggested for reconstructing an image of a moving object using a video sequence with compensation for the blurring effect, which does not require a priori information about the parameters of blurring. The algorithm is based on a combination of recurrent procedures of moving object detection, image restoration, and registration, which ensures its efficiency.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: Algorithms are considered that solve the problem of detecting structural changes in multitemporal satellite images of the Earth’s surface. A computational method is formulated for comparing such algorithms; to this end, a special mathematical model of structural changes is introduced that allows one to maximally approximate an experiment to real problems. Optimal parameters of operation of the algorithms are determined by this method, and the results of comparison of the performance of the algorithms are presented. Experiments on real data show that the algorithms proposed are suitable for practical application.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: The recognition system is able to both increase safety by compensating for a driver’s possible inattention and to decrease a driver’s tiredness by helping him follow traffic. An efficient algorithm for preprocessing digital images for the online detection of the road signs has been presented. It has been examined whether it is possible to use color space hue-saturation value for to select the red color. The algorithm for eliminating noises and increasing the accuracy and rate of detection has been developed. The obtained images are very suitable for the localization of road signs.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: An efficient two-stage non-blind deblurring framework is proposed for recovering blurred images progressively. To this date, most approaches commonly solve a single variational regularization problem incorporated with chosen priors, limiting the attained restoration quality. To address this, two different priors are adopted in separated stages to restore an image in a coarse-to-fine manner and each stage follows a variational regularization scheme. In the first stage, salient edges and large scale textures are produced by minimizing the e 0 norm of gradient. The intermediate result is then refined by non-local auto regression model in the next stage. Finally, experimental results demonstrate that the proposed methodology is efficient and achieves nice performance.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis
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    ABSTRACT: New algorithm for anesthesia depth analysis using EEG signal is presented. The algorithm is intended for the use in anesthesia depth monitors in the course of surgical operations. The suggested algorithm is based upon the combination of the following three approaches: signal randomness analysis with the use of approximate entropy, power spectrum analysis and analysis of specific signal changes that take place at the state of deep anesthesia. The algorithm was tested with the use of real ECG recordings obtained in the course of surgical operations and demonstrated good performance. The software package realizing this algorithm is used in an anesthesia depth monitor prepared to the batch production. Further efforts for the algorithm improving should be directed to the increase of the algorithm robustness to noises.
    No preview · Article · Oct 2015 · Pattern Recognition and Image Analysis