Pattern Recognition and Image Analysis (Pattern Recogn Image Anal )

Publisher: Nauchnyĭ sovet po kompleksnoĭ probleme "Kibernetika" (Akademii︠a︡ nauk SSSR), Springer Verlag

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.

  • Impact factor
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  • 5-year impact
    0.00
  • Cited half-life
    0.00
  • Immediacy index
    0.00
  • Eigenfactor
    0.00
  • Article influence
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  • 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

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Authors own final version only can be archived
    • Publisher's version/PDF cannot be used
    • On author's website or institutional repository
    • On funders designated website/repository after 12 months at the funders request or as a result of legal obligation
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (The original publication is available at www.springerlink.com)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: An algorithm for environmental map construction is suggested based on the comparative analysis of the relationships between environmental factors and categories of biota, the integral measure of consistency between random events and the measure of similarity between descriptive sets being used. For probability measures, the variance formulae are given. A fragment of an environmental map is constructed for the given region of the taiga ecosystem.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: By combining Histogram of Oriented Gradient (HOG), which is based on evaluating well-normalized local histograms of image gradient orientations in a dense grid, with Local Gabor Binary Pattern Histogram Sequence (LGBPHS), which concatenate the histograms of all the local regions of all the local Gabor magnitude binary pattern maps, as a feature set, we proposed a novel human detection feature. We employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, to project the feature onto a much lower dimensional subspace (9 dimensions, reduced from the original over 12000). We test the new feature in INRIA person dataset by using a linear SVM, and it yields an error rate of 1.35% with a false negatives (FN) rate of 0.46% and a false positive (FP) rate of 0.89%, while the error rate of HOG is 7.11% with a FN rate of 4.09% and a FP rate of 3.02%, and the error rate of LGBPHS is 13.55% with a FN rate of 4.94% and a FP rate of 8.61%.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: The proposed histogram-based algorithm searches for the clustering detailedness that differs in subdomains of the vector space of spectral features depending on the average separability of clusters. The objective of the hierarchical decomposition of clusters is to achieve limit detailedness with respect to the given cluster separability. Application of the algorithm to the unsupervised classification of land cover using five-spectral satellite remote sensing data is illustrated.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: In this paper it is presented a new algorithm for the construction of panoramic images (including 360 panoramas), which has as main characteristic to avoid the distortion that occurs by joining of several successive images in a sequence. We used the SIFT and RANSAC algorithms to find overlap areas between pairs of images, as well as a Blend algorithm for smoothing the joints. As the proposed algorithm doesn’t cause distortions, the subsequent correction is not necessary, contributing to a better performance. The results of experiments using commercial software and also the proposed algorithm were compared through a visual analysis. In addition, a quantitative analysis was done using numeric measures calculated on a panoramic image, generated from an image sequence of a region mapped and georeferenced by Google Earth.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: Availability of visual information means an effective way to improve specialized work in quite different areas. They enable reality modeling, a faster context analysis, a more successful search for problem solutions, an objective oriented decision making, as well as important elements for instructional processes. Avisual model is a simulation form that stimulate insights, say, creative answers to yet open questions and derivation of yet not imagined possible facts concerning a studied reality. Visual features of human body are highly relevant as basic information for a variety of decisions on activities in health domain. Through mechanisms of user-friendly interface one can promptly access and eventually perform adjustments. One can achieve better life quality and greater efficiency in health service from state as well. This paper describes an approach towards a visualization process of skin lesions, including features recognition for therapeutic processes and relevant decisions, with experimental validation.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: In this paper a new method for estimating the parameters of a synthetic deformable model of human head using a set of photographs is proposed. As opposed to statistical models, synthetic models have a well defined correspondence between the parameters and morphological features of the human head, though this bounds variability and complicates the task of fitting the model to photographs. A novel heuristic coarse-to-fine algorithm based on the psychophysical process of face perception and recognition by humans is proposed. A user study for evaluating the perceived quality of the generated models is also designed and conducted. Applicability of the proposed method for human-head modeling from photographs is corroborated by results of experiments on real world data.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: Speaker recognition is carried out in the space of the functional parameters of the area of the glottal cross-section, found by solving the inverse problem. This problem is solved in two stages: first, the signal obtained by inverse filtering is approximated using the vocal source model, and then the glottal area model parameters, which generate the calculated vocal source impulse, are computed. Speaker recognition is carried out on a database of Russian numerals from 0 to 9 separately for men (48 speakers) and women (37 speakers) at the segments of stressed vowels. Various methods of recognition are studied: the Gaussian mixture model (GMM), support vector machines (SVMs), discriminant analysis, naive Bayes classifier (NB), the method of classification trees (CTREE), and the Parzen window classifier. The best results were obtained using the method of SVMs and the Parzen method: the average total error of identification of men was 4.9% and 5.1%, and that of women—8.2% and 8.8%, respectively.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: An algorithm has been developed for finding photoluminescent objects on images of travel documents obtained under ultraviolet light. The objects found are classified depending on the sharpness of their boundaries.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: The paper presents new modifications of a previously developed method of optimal valid partitioning (OVP), including a new way of verifying two-dimensional patterns, which allows statistically valid estimation of the contribution into the pattern of each of the relevant variables. The means for analyzing the systems of patterns found by using the OVP method are also presented. A new variant of the OVP method, which allows searching for statistically valid differences between the patterns that exist in two groups of objects, is considered. An example of the practical use of the OVP versions in biomedical research is given. The differences between the risk factors for ischemic stroke in the group of patients who had transient ischemic attack (TIA) and in the group of patients without TIA are revealed.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: Although a variety of promising approaches exist, it is still a hard work to obtain desirable results in the area of pedestrian detection, especially in crowded and cluttered scene. In this paper, we present a detector which includes a discriminative shape descriptor—Local Segmentation Self-Similarity (LSSS) and induces a simple but sophisticated sample strategy. The descriptor represents the local shape of the object based on saliency on log-polar coordinate. The image is divided into disjoint cells, and the AdaBoost algorithm is adopted to integrate the local shape feature into a simple and powerful classifier. In detecting step, a greedy procedure is utilized for eliminating the repeated detections via non-maximum suppression. Experiments show that our approach achieves the considerable improvements in dealing with heavy occlusion and mutative background.
    Pattern Recognition and Image Analysis 01/2014;
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    ABSTRACT: The trading hubs construction problem for electricity markets under locational marginal prices is considered. Given historical prices for all nodes of the electricity grid and for all market participants over a sufficiently long period of time, the problem is to choose a required number of node clusters (hubs) and to assign market participants to hubs so as to minimize the deviation of hub prices from the prices of participants under certain constraints. In view of problem complexity, two evolutionary algorithms are proposed: a genetic algorithm and a hybrid local search heuristic. It is proved that the proposed genetic algorithm converges to optimum almost surely. The algorithms are tested and compared on the real-life data. The structure of the fitness landscapes is analyzed using multiple restarts of the local search and the behavior of the evolutionary algorithms is explained on the basis of this analysis.
    Pattern Recognition and Image Analysis 01/2014; 24(2):270-282.
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    ABSTRACT: The implementation of an image rotation method based on the chirp Z-transform is presented. Low computational costs of the algorithm are achieved by performing the chirp Z-transform using convolution in the Fourier domain. The series of numerical experiments supports the preservation of the image quality, even for multiple rotations.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: In this paper, we propose a new approach using a local version of the region-based active contour for object segmentation in images presenting heterogeneity in both the object of interest and the background. The local version was recently developed to deal with heterogeneous appearances by relying on extracting the local instead of global image statistics where the local extracted area is defined by using a disk, sketched at each point along the active contour, with a constant radius size inside and outside the contour. However, the use of a constant radius may prevent the active contour getting more information from its neighborhood and thus being trapped by undesired boundaries. To avoid this error segmentation, the local extracted area using our proposed approach is determined based on using two different radii to extract separately the interior and the exterior local information of the active contour. Using synthetic and real images, our approach shows an improvement in term of computation time and outperforms the conventional methods in noisy images presenting heterogeneity in both the object of interest and the background and using inadequate contour initialization.
    Pattern Recognition and Image Analysis 01/2014;
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    ABSTRACT: For the problem of image registration (binding), we examine whether it is possible to increase the prediction accuracy of binding parameters between two centers of local image fragments for which binding parameters have been determined. We study how to use linear and cubic polynomials, a Bezier curve, a bi-arc curve, and a rational Bezier curve for this purpose.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: This paper presents a method for revelation a sense standard based on a set of semantically equivalent phrases of a subject-oriented language as a foundation for text recognition and compression. It suggests using this method to organize the basic components of a software knowledge control system based on test tasks of the open form.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: The combinatorial theory of solvability, which stems from the algebraic approach to recognition problems, is a modern tool for studying feature descriptions of objects. In this paper, we formulate the basics of a formalism for applying methods of combinatorial theory of solvability to applications of graph theory in chemoinformatics. In the first part of the paper it is shown that, in light of the fundamental physicochemical features of the molecular structure, in order to describe the chemical structure of molecules it is appropriate to introduce a special concept of a χ-graph (chemograph): a special kind of a labeled graph. The fundamental properties of chemographs are considered, and special types of labeling of χ-subgraphs are introduced: χ-chains (chains of labeled vertices) and χ-nodes (subgraphs of the neighborhoods of labeled vertices). An axiomatic is suggested for introducing chemograph labeling functions on the basis of the fundamental postulates of chemical bonding theory. The basics of a theoretical apparatus for representing chemographs as labeling sets are presented.
    Pattern Recognition and Image Analysis 01/2014; 24(1).
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    ABSTRACT: The problem of the localization of image artifacts obtained in different spectral ranges on the basis of an information-theoretical difference measure is considered. Use of the conditional entropy value, calculated on a pair of images, is proposed for the measure of difference. The applicability conditions of this measure are specified and its testing is carried out. An example of the application is presented, using the difference measure to localize the repainting and retouching of areas in the images of fine-art paintings in the visible and ultraviolet bands.
    Pattern Recognition and Image Analysis 01/2014; 24(1):133-143.

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