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Descriptive image analysis: Part IV. Information Structure for Generating Descriptive Algorithmic Schemes for image Recognition

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pattern RECOGNITION AND image analysis 2020年第4期30卷 638-654页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is the fourth in a series of publications devoted to the state of the art and prospects for developing Descriptive image analysis, one of the leading and intensively developing fields of modern mathematical image analysis theory. The fundamental problem discussed in the article is to automate information extraction from images necessary for making intelligent decisions. This study is devoted to regularizing the generation of descriptive algorithmic image analysis and recognition schemes. The main result is the definition of a new mathematical structure with the following functional capabilities: 1) solution of an image recognition problem in a given formulation, with given initial data and a scenario that determines the sequence of application of information processing procedures and their iterative loops;2) construction of descriptive algorithmic schemes for solving a problem with given initial data in the absence of a given scenario;in the case of a successful solution, the fixation of the sequence of procedures and information processing loops that yielded its solution governs the corresponding descriptive algorithmic schemes and scenarios that can be further used to solve the corresponding class of image recognition problems;3) comparative analysis and optimization of methods for solving image recognition problems via their realization as descriptive algorithmic schemes and scenarios allowed by the structure. The introduced structure is a tool for representing and implementing information processing while solving an image recognition problem for arbitrary formulations, scenarios, models, and solution methods;it can also emulate any descriptive algorithmic scheme and combinations thereof, which are used and generated when solving an image recognition problem. The introduced structure is interpreted as a fundamental model for generating and emulating image recognition procedures. A type characteristic of the introduced information structure for genera

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated information extraction from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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Automation of Eye Disease Diagnoses Using Descriptive image Algebras and Boolean Algebra methods

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pattern RECOGNITION AND image analysis 2024年第2期34卷 299-308页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

The article presents an algebraic model for solving the problem of automation of ophthalmological diagnostics written in the language of descriptive image algebras. Descriptive image algebras are an initial mathematical language for formalizing and standardizing representations and procedures for processing image models and conversions over them when extracting information from images. To construct an algebraic model for solving the problem of automation of ophthalmological diagnostics, descriptive algebras of images with one ring are mainly used. This class of algebras belongs to the class of universal linear algebras with a sigma-associative ring with identity. A series of conversions and steps of the algebraic model are described using descriptive Boolean algebras over images. Descriptive image algebras are the main section of the mathematical apparatus of descriptive image analysis, which is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The article defines specialized versions of descriptive image algebras with one ring and descriptive Boolean algebras over images, over models and representations of images, and over conversions of image models and images themselves, necessary for constructing an algebraic model. The image models (representations, formalized descriptions) used in writing the article are described. An example of a descriptive algorithmic scheme for solving an applied ophthalmological problem using an algebraic model is constructed.

关键词： image analysis descriptive image analysis mathematical theory of image analysis descriptive algorithmic schemes descriptive image models image representations representations of image analysis procedures descriptive approach to image analysis and understanding image algebras descriptive image algebra with one ring optical coherence tomography angiography fundus

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Automatic recognition and quantitative analysis of Ω phases in Al-Cu-Mg-Ag alloy

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Journal of Central South University 2014年第5期21卷 1696-1704页

作者：刘冰滨谷艳霞刘志义田小林 Faculty of Information Technology Macao University of Science and Technology College of Materials Science and Engineering Central South University

The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.

关键词： auto pattern recognition top-hat transformation second phases in A1 alloy quantitative analysis

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Multivariate Slow Feature analysis and Decorrelation Filtering for Blind Source Separation

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IEEE TRANSACTIONS ON image PROCESSING 2013年第7期22卷 2737-2750页

作者： Ha Quang Minh Wiskott, Laurenz Ist Italiano Tecnol Dept Pattern Anal & Comp Vis I-16163 Genoa Italy Ruhr Univ Bochum Inst Neuroinformat D-44780 Bochum Germany

We generalize the method of Slow Feature analysis (SFA) for vector-valued functions of several variables and apply it to the problem of blind source separation, in particular to image separation. It is generally necessary to use multivariate SFA instead of univariate SFA for separating multi-dimensional signals. For the linear case, an exact mathematical analysis is given, which shows in particular that the sources are perfectly separated by SFA if and only if they and their first-order derivatives are uncorrelated. When the sources are correlated, we apply the following technique called Decorrelation Filtering: use a linear filter to decorrelate the sources and their derivatives in the given mixture, then apply the unmixing matrix obtained on the filtered mixtures to the original mixtures. If the filtered sources are perfectly separated by this matrix, so are the original sources. A decorrelation filter can be numerically obtained by solving a nonlinear optimization problem. This technique can also be applied to other linear separation methods, whose output signals are decorrelated, such as ICA. When there are more mixtures than sources, one can determine the actual number of sources by using a regularized version of SFA with decorrelation filtering. Extensive numerical experiments using SFA and ICA with decorrelation filtering, supported by mathematical analysis, demonstrate the potential of our methods for solving problems involving blind source separation.

关键词： image separation blind source separation slow feature analysis independent component analysis filtering generalized eigenvalue problem

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Hand vein authentication using biometric graph matching

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IET BIOMETRICS 2014年第4期3卷 302-313页

作者： Lajevardi, Seyed Mehdi Arakala, Arathi Davis, Stephen Horadam, Kathy J. RMIT Univ Sch Math & Geospatial Sci Melbourne Vic Australia

This study proposes an automatic dorsal hand vein verification system using a novel algorithm called biometric graph matching (BGM). The dorsal hand vein image is segmented using the K-means technique and the region of interest is extracted based on the morphological analysis operators and normalised using adaptive histogram equalisation. Veins are extracted using a maximum curvature algorithm. The locations and vascular connections between crossovers, bifurcations and terminations in a hand vein pattern define a hand vein graph. The matching performance of BGM for hand vein graphs is tested with two cost functions and compared with the matching performance of two standard point patterns matching algorithms, iterative closest point (ICP) and modified Hausdorff distance. Experiments are conducted on two public databases captured using far infrared and near infrared (NIR) cameras. BGM's matching performance is competitive with state-of-the-art algorithms on the databases despite using small and concise templates. For both databases, BGM performed at least as well as ICP. For the small sized graphs from the NIR database, BGM significantly outperformed point pattern matching. The size of the common subgraph of a pair of graphs is the most significant discriminating measure between genuine and imposter comparisons.

关键词： cameras graph theory image matching image segmentation infrared imaging iterative methods mathematical morphology vein recognition BGM matching performance ICP K-means technique NIR database adaptive histogram equalisation automatic dorsal hand vein verification system biometric graph matching dorsal hand vein image segmentation far infrared NIR cameras hand vein authentication hand vein graph iterative closest point maximum curvature algorithm modified Hausdorff distance morphological analysis operators near infrared NIR cameras public databases small sized graphs standard point pattern matching algorithms vascular connections vein extraction Cameras Graph matching iterative methods mathematical morphology Graph theory Iterative closest point algorithm ACSBG1 gene Infrared imaging image matching image segmentation Cost functions Inductively coupled plasma hand veins Database

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methods of handling of in-bulk agglomeration layer image representation for granulometric composition assessment

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pattern Recognition and image analysis 2009年第1期19卷 103-105页

作者： Ershov, E.V. Selivanovskikh, V.V. Ganicheva, O.G. Cherepovets State University State Educational Institution of Federal Educational Agency ul. Lunacharski 5 Vologda oblast Cherepovets 162600 Russia

The analysis of methods of images handling is carried out, the method of dynamic threshold division which is based on the calculation of weight coefficient of points in the local area is suggested. The method of adaptive adjustment of the size of the local area of the image representation is carried out to define the threshold value. Also methods of binary recursive filtration and mathematical morphology to reduce interference are introduced. © 2009 Pleiades Publishing, Ltd.

关键词： image representation

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Laplacian Coordinates: Theory and methods for Seeded image Segmentation

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IEEE TRANSACTIONS ON pattern analysis AND MACHINE INTELLIGENCE 2021年第8期43卷 2665-2681页

作者： Casaca, Wallace Gois, Joao Paulo Batagelo, Harlen Costa Taubin, Gabriel Nonato, Luis Gustavo Sao Paulo State Univ UNESP Dept Energy Engn BR-01049010 Rosana Brazil Fed Univ ABC UFABC Ctr Math Comp & Cognit BR-09210580 Santo Andr e Brazil Brown Univ Sch Engn Providence RI 02912 USA Univ Sao Paulo ICMC BR-13566590 Sao Carlos Brazil

Seeded segmentation methods have gained a lot of attention due to their good performance in fragmenting complex images, easy usability and synergism with graph-based representations. These methods usually rely on sophisticated computational tools whose performance strongly depends on how good the training data reflect a sought image pattern. Moreover, poor adherence to the image contours, lack of unique solution, and high computational cost are other common issues present in most seeded segmentation methods. In this work we introduce Laplacian Coordinates, a quadratic energy minimization framework that tackles the issues above in an effective and mathematically sound manner. The proposed formulation builds upon graph Laplacian operators, quadratic energy functions, and fast minimization schemes to produce highly accurate segmentations. Moreover, the presented energy functions are not prone to local minima, i.e., the solution is guaranteed to be globally optimal, a trait not present in most image segmentation methods. Another key property is that the minimization procedure leads to a constrained sparse linear system of equations, enabling the segmentation of high-resolution images at interactive rates. The effectiveness of Laplacian Coordinates is attested by a comprehensive set of comparisons involving nine state-of-the-art methods and several benchmarks extensively used in the image segmentation literature.

关键词： image segmentation Laplace equations Minimization mathematical model Tools Electronic mail Computational modeling Seeded image segmentation graph laplacian laplacian coordinates energy minimization models

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An OOPR-based rose variety recognition system

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2006年第1期19卷 79-101页

作者： Miao, ZJ Gandelin, MH Yuan, BZ Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

This paper describes a rose analysis and recognition system and presents the main principles used to realize the recognition system. The major principles presented in this paper are the mathematical description methods for rose features such as shape, size and color of the flower, petal, leaf, etc, and the object-oriented pattern recognition (OOPR) approach which mathematically deals with how to comprehensively use all different rose features rationally in the recognition scheme. The recognition system is described and some of its experimental results are given which demonstrate the efficiency of our methods. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： pattern recognition intelligent systems image processing color image artificial intelligence image segmentation

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Data analysis and Interpretation: methods of Computer-Aided Measuring Transducer Theory, Morphological analysis, Possibility Theory, and Subjective mathematical Modeling

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pattern RECOGNITION AND image analysis 2023年第4期33卷 1515-1563页

作者： Pyt'ev, Yu. P. Chulichkov, A. I. Falomkina, O. V. Balakin, D. A. Lomonosov Moscow State Univ Fac Phys Moscow 119991 Russia

This article provides an overview of the fundamental research directions being pursued at the Faculty of Physics of Lomonosov Moscow State University under the guidance of Professor Yuri Petrovich Pyt'ev. These research directions can be categorized into three primary areas: methods of morphological analysis of images and signals, theory of computer-aided measuring systems, and methods related to the theory of possibilities and subjective mathematical modeling. The article elucidates the foundational ideas and concepts of these directions, contemplates alternative approaches to address similar challenges, and offers both model-based and application-driven examples utilizing the methods corresponding to these directions and their combinations.

关键词： morphological image analysis measurement reduction theory of possibilities subjective modeling optimal solutions hypothesis testing

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Dynamics, Mechanics, Control, and mathematical Modeling-Scientific and Pedagogical School of Yuri Isaakovich Neimark

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pattern RECOGNITION AND image analysis 2023年第4期33卷 1479-1485页

作者： Savelyev, V. P. Vasin, D. Yu. Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod 603952 Russia

Information about the Nizhny Novgorod Scientific and Pedagogical School "Dynamics, Mechanics, Control and mathematical Modeling" is presented. The founder of the school is Honored Scientist of the Russian Fe... 详细信息

关键词： scientific and pedagogical school theory of stability theory of automatic regulation and control dynamics of nonholonomic systems theory of oscillations and dynamic systems search optimization methods pattern recognition and medical diagnostics mathematical modeling

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