Pattern Recognition and Image Analysis Journal Impact & Description - ResearchGate - Impact Rankings (
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Pattern Recognition and Image Analysis (Pattern Recogn Image Anal)
Publisher: Nauchnyi? sovet po kompleksnoi? probleme &Kibernetika& (Akademii?a? 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.
Journal Impact: 0.36*
*This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive.
Journal impact history
2016 Journal impact Available summer 2017
2015 Journal impact 0.36
2014 Journal impact 0.36
2013 Journal impact 0.27
2012 Journal impact 0.34
2011 Journal impact 0.47
2010 Journal impact 0.50
2009 Journal impact 0.37
2008 Journal impact 0.50
2007 Journal impact 0.40
2006 Journal impact 0.16
Journal impact over time
Journal impact
Additional details
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Other titles Raspoznavanie obrazov i analiz izobrazhenii?
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper
Publisher details This publication is classified Romeo Blue.
Publications in this journal
ABSTRACT: A new algorithm for detecting linear infrastructural objects in aerial photos is presented. It is assumed that these objects pass through the whole image: beginning at one side and finishing at the opposite one. It is also assumed that the altitude of shooting and the image scale are invariable. The presented algorithm synthesizes the operation of an edge detector, a ridge detector, and the Hough accumulator into an object-of-interest mask excluding lots of spurious responses, and it completes missing information missed by detectors. First of all the image is preprocessed and anisotropically and repeatedly shrunk along the direction of the linear object and synthesis is performed by finding the shortest paths in a graph. The graph is presented in the form of a mesh, where each mesh node corresponds to a pixel of the shrunk image. At each node on the edges and in ridge lines, its energy is calculated, which is the reliability of this pixel. Then, the path that maximizes the sum of energies at the nodes is determined by considering its curvature. The obtained paths form a mask of linear objects. The algorithm is verified by using aerial photos for different seasons, and it demonstrates proper results (accuracy is ~80%).
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: The ideal of Bessel-Fourier moments (BFMs) for image analysis and only rotation invariant image cognition has been proposed recently. In this paper, we extend the previous work and propose a new method for rotation, scaling and translation (RST) invariant texture recognition using Bessel-Fourier moments. Compared with the others moments based methods, the radial polynomials of Bessel-Fourier moments have more zeros and these zeros are more evenly distributed. It makes Bessel-Fourier moments more suitable for invariant texture recognition as a generalization of orthogonal complex moments. In the experiment part, we got three testing sets of 16, 24 and 54 texture images by way of translating, rotating and scaling them separately. The correct classification percentages (CCPs) are compared with that of orthogonal Fourier-Mellin moments and Zernike moments based methods in both noise-free and noisy condition. Experimental results validate the conclusion of theoretical derivation: BFM performs better in recognition capability and noise robustness in terms of RST texture recognition under both noise-free and noisy condition when compared with orthogonal Fourier-Mellin moments and Zernike moments based methods.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: Presently there already existed a few human ear image databases, which have been very influential in advancing the research on ear recognition. However no ear video databases are available. In this paper, we introduce the construction and basic content of the Chinese Ear Video Database (CEVD) and some primary evaluation results on it. The CEVD consists of 3600 ear video segments collected from 120 subjects. All the video segments in the database were collected in specially designed environment with three principal variations of illumination condition, viewing angle and interference. Compared with other public ear database, CEVD can not only be used for image-based applications but also video-based applications. In this paper we introduce the database and describe the collecting procedure. It excels in its large-scale and variation modes and is expected to have positive impact on the development and evaluation of ear recognition algorithms. This paper also gives experiment results of improved CamShift and AdaBoost algorithm on the database.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: Image segmentation is one of many fundamental problems in computer vision. The need to divide an image to a number of classes is often a part of a system that uses image processing methods. Therefore, lots of methods were developed that are based on different approaches. The image segmentation could be classified with respect to many criteria. One such a criterion is based on the degree of allowed interactivity. The interactivity could be of several types—interactive initialization, interaction while the computation is running or manual refinement of achieved results, for example. Especially the precise initialization plays an important role in many methods. Therefore the possibility to initialize the method manually is often invaluable advantage and information obtained this way could be the difference between good and poor results. Unfortunately, in many cases it is not possible to initialize a method manually and the process needs to be automated. In this paper, an approach for such an automation is presented. It is based on shortest paths in a graph and deriving an area of influence for each obtained seed point. This method is called shortest path basins.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: An approach of solving the problem of multiclass supervised classification, based on using errorcorrecting codes is considered. The main problem here is the creation of binary code matrix, which provides high classification accuracy. Binary classifiers must be distinct and accurate. In this issue, there are many questions. What should be the elements of the matrix, how many elements provide the best accuracy and how to find them? In this paper an approach to solve some optimization problems for the construction of the binary code matrix is considered. The problem of finding the best binary classifiers (columns of matrix) is formulated as a discrete optimization problem. For some partial precedent classification approach, there is a calculation of the effective values of optimising function. Prospects of this approach are confirmed by a series of experiments on various practical tasks.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: In this paper, a fast and reliable method for hand detection based on continuous skeletons approach is presented. It demonstrates real-time working speed and high detection accuracy (3–5% both FAR and FRR) on a large dataset (50 persons, 80 videos, 2322 frames). These make it suitable for use as a part of modern hand identification systems including mobile ones. Overall, the study shows that continuous skeletons approach can be used as prior for object and background color models in segmentation methods with supervised learning (e.g., interactive segmentation with seeds or abounding box).
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: Method of fuzzy boosting providing iterative weak classifiers selection and their quasi-linear composition construction is presented. The method is based on the combination of boosting and fuzzy integrating techniques, when at each step of boosting weak classifiers are combined by Choquet fuzzy integral. In the proposed FuzzyBoost algorithm 2-additive fuzzy measures were used, and method for their estimation was proposed. Although detailed theoretical verification of proposed algorithm is still absent, the experimental results, made on simulated data models, demonstrate that in the case of complex decision boundaries FuzzyBoost significantly outperforms AdaBoost.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: In the context of the algebraic approach to recognition of Yu.I. Zhuravlev’s scientific school, metric analysis of feature descriptions is necessary to obtain adequate formulations for poorly formalized recognition/classification problems. Formalization of recognition problems is a cross-disciplinary issue between supervised machine learning and unsupervised machine learning. This work presents the results of the analysis of compact metric spaces arising during the formalization of recognition problems. Necessary and sufficient conditions of compactness of metric spaces over lattices of the sets of feature descriptions are analyzed, and approaches to the completion of the discrete metric spaces (completion by lattice expansion or completion by variation of estimate) are formulated. It is shown that the analysis of compactness of metric spaces may lead to some heuristic cluster criteria commonly used in cluster analysis. During the analysis of the properties of compactness, a key concept of a ρ-network arises as a subset of points that allows one to estimate an arbitrary distance in an arbitrary metric configuration. The analysis of compactness properties and the conceptual apparatus introduced (ρ-networks, their quality functionals, the metric range condition, i- and ρ-spectra, ε-neighborhood in a metric cone, ε-isomorphism of complete weighted graphs, etc.) allow one to apply the methods of functional analysis, probability theory, metric geometry, and graph theory to the analysis of poorly formalized problems of recognition and classification.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: The problems of learning a good similarity function between objects naturally arise in machine learning, pattern recognition and data mining such as clustering, community detection or metric learning as well. We focus on the special case of this problem, where similarity function is completely determined by the hidden object classes. But we assume that no information about object labels is accessible on a training stage. The main contribution of the paper is two-stage algorithm assigns to each object its class label and provides a similarity function based on this assignment. We provide risk bounds and empirical evaluation in support of our algorithm. As a consequence of our analysis we provide a new tradeoff between empirical error of a multi-class classifier and its generalization error.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: The problem of speaker recognition from a given set of speakers for any language and any context is considered. A database of Russian numerals that contains speech segments from 216 men and 177 women, each of whom spoke from 400 to 800 words, is used for recognition. Speech has been recorded on different types of microphones in different rooms at the natural noise level. Recognition is based on solutions of the inverse problem of finding the voice excitation pulse shape for each pitch period by the known speech segment. The pulse shape is defined as the inverse Fourier transform of the regularized ratio of speech signal spectra at the intervals of the open and closed glottis. Recognition is carried out by ten parameters: the pitch period, the open glottis interval duration, times when the source amplitude is maximum, minimum, or zero, the amplitude ratio for the minimum and maximum source pulses, three decomposition ratios of the source function by the principal component method, and the vowel duration. In such a recognition procedure, in the case of the utterance of a word that contains one vowel, the false reject rate (FRR) for men is 1.7–5.4%, and the false acceptance rate (FAR) is 5.4–7.1%. For women FRR = 2–5.2% and FAR = 5.2–6.3%. The recognition error decreases with an increasing number of vowels in the speech signal. At 10 vowels, for men FRR = 0.05–0.2% and FAR = 0.07–0.8%, and for women FRR = 0.09–0.2% and FAR = 0.17–2.1%.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: In this paper we present an evaluation of six well established line segment distance functions within the scope of line segment matching. We show analytically, using synthetic data, the properties of the distance functions with respect to rotation, translation, and scaling. The evaluation points out the main characteristics of the distance functions. In addition, we demonstrate the practical relevance of line segment matching and introduce a new distance function.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: A large percentage of photos on the Internet cannot be reached by search engines because of the semantic gap due to the absence of textual meta-data. Despite of decades of research, neither model based approaches can provide quality annotation to images. Many segmentation algorithms use a low-level predicates to control the homogeneity of the regions. So, the resulting regions are not always being semantically compact. The first proposed approach to resolve this problem is to regroup the adjacent region of image. Many features extraction method and classifiers are also used singly, with modest results, for automatic image annotation. The second proposed approach is to select and combine together some efficient descriptors and classifiers. This document provides a hybrid semantic annotation system that combines both approaches in hopes of increasing the accuracy of the resulting annotations. The color histograms, Texture, GIST and invariant moments, used as features extraction methods, are combined together with multi-class support vector machine, Bayesian networks, Neural networks and nearest neighbor classifiers, in order to annotate the image content with the appropriate keywords. The accuracy of the proposed approach is supported by the good experimental results obtained from two image databases (ETH-80 and coil-100 databases).
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: Linear transformation of data in multidimensional feature space based on Fisher’s criterion is considered. The case of two classes with arbitrary distributions is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Example offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher’s linear discriminant.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: The authors propose a new face recognition system with an evaluation function using feature points. The feature points are detected automatically by Milborrow’s Stasm software. Before recognition, rotation compensation and size normalization are applied to the feature points. The main method is to calculate the squared error between the registered face and the input face as to length of a characteristic pair of feature points on face. The False Rejection Rate (FRR) for the registered and input face of the same person, and the False Acceptance Rate (FAR) for the registered face and a different person’s input face are evaluated. The input is a video sequence. Stable recognition is obtained with small FRR and FAR for the video of a period of 0.5 s.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: Prediction of the properties of chemical compounds by mathematical methods of pattern recognition is considered. The investigation was carried out by the example of the activity of cell division enzyme inhibitors. An approach based on mixtures of algorithms is used as the method for the construction of recognition models. A two-phase solution procedure for the structure–property problem is analyzed. The local classifier based on the nearest neighbor algorithm and the method of clustering sets is also described. New algorithms for the construction of classifier mixtures are compared. The methods of coordinated prediction of the activity of new compounds are examined. A comparison of mathematical modeling results with molecular design methods based on the coordination of compounds with known structures of therapeutic targets is also presented. An experimental study of the biological activity is conducted.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: A method for determining the pupil boundary in the image of eye is proposed. The method is based on image binarization followed by a search of the pupil as one of the connectivity components. The pupil boundary is determined as a part of boundary of the connectivity component. Hough transform is used for separating pupil in the case of its merging in one connectivity component with other objects, as well as to verify the likelihood of solution.
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: This work is directed at creation of methods of study of the processes in the ionospheric–magnetospheric system during increased solar and geomagnetic activity. Method of modeling and analysis of the parameters of the ionosphere, which allows prediction of the data and identification of the anomalies during the ionospheric disturbances, are given. Computational solutions for determination and estimation of the geomagnetic disturbances are described. Method of determination of the anomalous changes in the time course of cosmic rays, which allows qualitative estimations of the moments of their origination, duration, and intensity, is suggested.
On the basis of the methods elaborated, the data on the periods of strong and moderate magnetic storms are complexly analyzed. Sharp oscillations in the electron density of the ionosphere with positive and negative phases, which originate in the regions analyzed during an increase in geomagnetic activity, are distinguished. Positive phases of the ionospheric disturbances from several hours to one and a half days long were formed before the beginning of the magnetic storms. At the moments of the increase in the electron concentration, a local increase is observed in the level of cosmic rays (several hours before the magnetic storms) that supported the solar nature of these effects. During the strongest geomagnetic disturbances, the electron concentration in the ionosphere decreased significantly and led to prolonged negative phases of ionospheric storms, which coincided with the decrease in the level of cosmic rays (a Forbush decrease).
Article & Apr 2016
& Pattern Recognition and Image Analysis
ABSTRACT: This paper describes a practical aspect of identification and classifying of Guns based on gunshot wound patterns. We mark a genuinely digitized approach for the characteristic and set of guns used in homicidal cases using Gene expression programming. This approach develops a computationally attractive and effective alternative to investigate the guns used in crime which uses the images of gunshot wound patterns available on the human body. The experimental results achieved for identification and classification accuracy of 91.1 and 93.4%, respectively, on the available database of 30 images including three categories: Hard-contact, Loose-contact and Angled-contact of each pattern consisting of gunshot wounds. Our experimental results from the authentication experiments and false positive identification verses false negative identification also suggest the superiority of the proposed approach over the other popular feature extraction approach considered in this work.
Article & Apr 2016
& Pattern Recognition and Image Analysis
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
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