Flexible array self-organizing map neural network and its application in complex pattern recognition

Due to the fixed array of competing-layer structure, observations distribution features cannot be well reflected by conventional SOM in two-dimensional plane. Aimed at solving this problem, a novel flexible array SOM algorithm (faSOM) is proposed in this paper. This algorithm can adaptively adjust the positions of competing-layer neurons to keep consistent with position of observations. As a result, the neurons in mapping space can maintain the original observation’ features. The faSOM algorithm is successfully applied in pattern recognition of two artificial datasets and red-spotted stonecrop samples. Both theory analysis and experimental results indicate that faSOM is an effective algorithm which can map observation’s inherent feature quickly and accurately. Compared with conventional SOM algorithm, feature mapping effect of faSOM algorithm is much better, because it resolves a typical problem in the conventional SOM that the structure of mapped dataset in competing-layer is distorted.     

Walking pattern classification using a granular linguistic analysis

Classifying walking patterns helps the diagnosis of health status, disease progression and the effect of interventions. In this paper, we develop previous research on human gait to extract a meaningful set of parameters that allow us to design a highly interpretable system capable of identifying different gait styles with linguistic fuzzy if-then rules. The model easily discriminates among five different walking patterns, namely: normal walk, on tiptoes, dragging left limb, dragging right limb, and dragging both limbs. We have carried out a complete experimentation to test the performance of the extracted parameters to correctly classify these five chosen gait styles.     

Evolving pattern recognition systems

A hybrid evolutionary learning algorithm is presented that synthesizes a complete multiclass pattern recognition system. The approach uses a multifaceted representation that evolves layers of processing to perform feature extraction from raw input data, select cooperative sets of feature detectors, and assemble a linear classifier that uses the detectors' responses to label targets. The hybrid algorithm, called hybrid evolutionary learning for pattern recognition (HELPR), blends elements of evolutionary programming, genetic programming, and genetic algorithms to perform a search for an effective set of feature detectors. Individual detectors are represented as expressions composed of morphological and arithmetic operations. Starting with a few small random expressions, HELPR expands the number and complexity of the features to produce a recognition system that achieves high accuracy. Results are presented that demonstrate the performance of HELPR-generated recognition systems applied to the task of classification of high-range resolution radar signals.     

On fingerprint pattern recognition

Stochastic languages have been used for fingerprint classification⁽²⁾. In this note we observe that it is not possible to classify the fingerprint patterns in the absence of accurate registration of the fingerprint.Use of features like downward fork, upward fork, end, etc. have been suggested⁽⁴⁾ for fingerprint classification. It is noted that some of the results are either incorrect or incomplete. Limitations of the various algorithms are presented.     

Hybrid systems of pattern recognition

Hybrid systems of pattern recognition are proposed which ensure efficient use of a priori data about the form of decision functions and information of learning samples. The results obtained are generalized for processing heterogeneous data. Lapko Aleksandr Vasil’evich. Born 1949. Graduated from the Frunze Polytechnical Institute in 1971. Received Doctoral degree (in Technical Sciences) in 1990. Main Researcher at the Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences. Scientific interests: nonparametric statistics, pattern recognition systems, modeling and optimization of uncertain systems. Author of 215 publications including 12 monographs. President of the Krasnoyarsk regional division of the Russian Association for Pattern recognition and Image Analysis. Honored Worker of Science of the Russian Federation. Lapko Vasilii Aleksandrovich. Born 1974. Graduated from the Krasnoyarsk State Technical University in 1996. Received Doctoral degree (in Technical Sciences) in 2004. Senior Researcher at the Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences. Scientific interests: nonparametric statistics, pattern recognition systems, modeling of uncertain systems, methods of collective estimation. Author of 92 publications including 3 monographs. In 2005 awarded the medal of RAS for the best scientific work among young scientists in the field of informatics, computer technics, and automation.     

Moment invariants for pattern recognition

Invariant combinations of moments of arbitrary order are defined. Application to a vehicle image shows that a reconstructed image having <10% error may be obtained by using invariants formed from moments uop to order eight.     

MicroARTMAP for pattern recognition problems

Pattern recognition is an important aspect of a dominant technology such as machine intelligence. Domain specific fuzzy-neuro models particularly for the ‘black box’ implementation of pattern recognition applications have recently been investigated. In this paper, Sanchez’s MicroARTMAP has been discussed as a pattern recognizer/classifier for the image processing problems. The model inherently recognizes only noise free patterns and in case of noise perturbations (rotations/scaling/translation) misclassifies the images. To tackle this problem, a conventional Hu’s moment based rotation/scaling/translation invariant feature extractor has been employed. The potential of this model has been demonstrated on two problems, namely, recognition of alphabets and words and prediction of load from yield pattern of elasto-plastic analysis. The second example concerns with color images dealing with colored patterns. MicroARTMAP is also applied to other two civil engineering problems, namely (a) Indian Standard (IS) classification of soil and (b) prediction of earthquake parameters from the response spectrum in which no feature extractor step is necessary.     

Image normalization for pattern recognition

In general, there are four basic forms of distortion in the recognition of planar patterns: translation, rotation, scaling and skew. In this paper, a normalization algorithm has been developed which transforms pattern into its normal form such that it is invariant to translation, rotation, scaling and skew. After normalization, the recognition can be performed by a simple matching method. In the algorithm, we first compute the covariance matrix of a given pattern. Then we rotate the pattern according to the eigenvectors of the covariance matrix, and scale the pattern along the two eigenvectors according to the eigenvalues to bring the pattern to its most compact form. After the process, the pattern is invariant to translation, scaling and skew. Only the rotation problem remains unsolved. By applying the tensor theory, we find a rotation angle which can make the pattern invariant to rotation. Thus, the resulting pattern is invariant to translation, rotation, scaling and skew. The planar image used in this algorithm may be curved, shaped, a grey-level image or a coloured image, so its applications are wide, including recognition problems about curve, shape, grey-level and coloured patterns. The technique suggested in this paper is easy, does not need much computation, and can serve as a pre-processing step in computer vision applications.     

Artificial data for pattern recognition

The paper describes the generation of three types of artificial data and their use as test material in pattern recognition research. Type A data: The user defines the perfect decision surface. The classes are separable and the pdf's flat. This type is useful in two ways: (i) To investigate whether a learning procedure can achieve a minimal-cost solution. (ii) To compare the powers of two classifiers. Type B data: The user defines the optimal decision surface. The classes are not separable; the degree of overlap between the classes can be controlled by the user. The pdf's are approximately flat, except in regions close to this optimal decision boundary. This type is useful in the following ways: (i) To study the effect of varying the overlap between classes upon a learning procedure. (ii) To compare the powers of two classifiers on a random problem. Type C data: This type is a model of natural, clustered data. The user specifies the location, height, and spread of a number of “hills” in the pdf (for each class). These parameters allow us to calculate the pdf's and hence the Bayes' classification, at any given point. This provides a powerful tool for the objective evaluation of a learning classifier, operating on a realistic problem.     

Some methods for pattern recognition

The present study is dedicated to pattern recognition of alphabetical and numerical symbols. A recognition method based on modification of Hausdorff metrics is presented; then, another method named the method of radial neighborhoods is described. A series of experiments on test patterns are conducted.