Pattern Recognition System Research Articles

Image-assisted Biometric Identification

Biometrics is a rapidly developing technology that has seen widespread use in forensics applications such as criminal identification, secure access, and prison security. A biometric system is a pattern recognition system that recognizes a person by determining the authenticity of a physiological and/or behavioural feature that that person possesses. One of the most widely accepted biometrics utilized by humans in their visual interactions is image- assisted based (facial) biometric. Image-assisted biometric identification is the use of face recognition technology in capturing image of a unique feature of an individual such as an eye or face, and comparing it with a template captured earlier and stored a database. Face recognition is one of the more recent biometrics technologies. The system examines face features and tries to match them to a database of digitized images. This technology is quite new, having only been available commercially since the 1990s. Face recognition has gotten a lot of press after the 9/11 attacks because of its capacity to identify known terrorists and criminals. [1]. Although the technology is mostly utilized for security and law enforcement, there is growing interest in other applications. Keyword: Image-assisted based biometric identification, Face recognition technology, Image

AI-based computer vision using deep learning in 6G wireless networks

Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current networks such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency algorithms (DL-EA) for computer vision to address the issues mentioned and improve the system's outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven methodology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control.

Accurate Recognition of Motion Patterns Based on Artificial Visual Neural Network

In order to improve the accuracy of motion pattern recognition, this paper combines the artificial visual neural network to construct a motion pattern recognition system. Moreover, this paper discusses the psychological perception properties of human eyes to color stimuli and gives a description of the observation field of view where the color stimuli are located. At the same time, this paper analyzes the phenomenon of color adaptation and provides a method of color appearance matching through modeling to achieve color appearance matching under variable observation conditions. Based on the chromatic adaptation transformation, a chromatic appearance model is given, which can predict the corresponding color and also predict the chromatic appearance properties of color stimuli under given observation conditions. In addition, this paper constructs an intelligent motion pattern recognition system combined with artificial visual neural network. The experimental results show that the motion pattern recognition system based on artificial visual neural network can accurately identify the motion pattern category.

Nonvolatile Frequency-Programmable Oscillator With NbO2 and Li-Based Electro-Chemical Random Access Memory for Coupled Oscillators-Based Temporal Pattern Recognition System

In this letter, we propose a compact frequency programmable oscillator with an NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -based insulator-metal transition (IMT) device and three-terminal Li-based electro-chemical RAM (Li-ECRAM) for coupled oscillators-based temporal pattern recognition system. Owing to the non-volatility, multilevel characteristics, and linear conductance modulation of Li-ECRAM, our proposed oscillator exhibits a large number of programmable frequencies (~45) and high controllability by applying pulses to the Li-ECRAM to attain the target frequency. Furthermore, we demonstrated injection locking phenomenon in our proposed oscillators, which can be utilized for the frequency detection of the injected signal. Finally, we simulated four-coupled oscillators system for the frequency classification of the input temporal signal with multiple frequencies and amplitude noise. These results demonstrate the feasibility of a temporal pattern recognition system composed of our proposed compact frequency-programmable oscillators.

A review of English language from the scanned document with the use of pattern recognition: Detailed study on recognition system

The technology of Document Analysis and Recognition, as a branch of pattern recognition, faces various practical demands in the real world, such as the digitization of books, newspapers, and archives, invoicing, and corporate documents. Pattern recognition and machine learning are among the most cutting-edge areas in software science. Statistical learning theory based neural network approaches and methodologies have recently come under more and more scrutiny. By utilizing the proper character recognition and segmentation modules for optical character recognition (OCR), it is essential to identify the document's language and printing style. In the field of pattern recognition, recognizing handwritten papers is a difficult problem. Algorithms and statistical models that computers use to complete a certain task without being explicitly focused on machine learning (ML). It is possible to utilize these algorithms for a variety of purposes, including data mining and image processing. It is easy to automate tasks by utilizing machine learning when an algorithm has learned how to deal with data. The purpose of this review article is to summarize and compare many well-known methodologies that are utilized at various phases of a pattern recognition system's development. After examining several strategies for pattern identification, it was determined that the most accurate method is optical character recognition (OCR). Optical character recognition (OCR) scanners, on the other hand, have a 99 percent accuracy rate. Diabetes Retinopathy (DR) also has the lowest accuracy, at just percent.

Development of a Machine Vision System for Image Recognition of Design Estimates

This paper discusses implementation of a machine vision system for pattern recognition of design estimates. The main problem in the development of these systems is the choice of unique features that remain invariant to various kinds of transformations. Angular descriptors were chosen as a dominant feature. The paper presents a comparative analysis of the corner detection methods of the Moravec algorithm, the Harris algorithm, and the Shi-Tomasi algorithm. The authors have developed software in Python language that implements the operation of the Harris detector and the Shih-Tomasi detector. The recognition system is being tested for building 3D models in Blender

Pattern Recognition of microRNA Expression in Body Fluids Using Nanopore Decoding at Subfemtomolar Concentrations.

This paper describes a method for detecting microRNA (miRNA) expression patterns using the nanopore-based DNA computing technology. miRNAs have shown promise as markers for cancer diagnosis due to their cancer type specificity, and therefore simple strategies for miRNA pattern recognition are required. We propose a system for pattern recognition of five types of miRNAs overexpressed in bile duct cancer (BDC). The information of miRNAs from BDC is encoded in diagnostic DNAs (dgDNAs) and decoded electrically by nanopore analysis. With this system, we succeeded in the label-free detection of miRNA expression patterns from the plasma of BDC patients. Moreover, our dgDNA–miRNA complexes can be detected at subfemtomolar concentrations, which is a significant improvement compared to previously reported limits of detection (∼10–12 M) for similar analytical platforms. Nanopore decoding of dgDNA-encoded information represents a promising tool for simple and early cancer diagnosis.

Method for creating synthetic data sets for training neural network models for object recognition

Introduction: The lack of training data leads to low accuracy of visual pattern recognition. One way to solve this problem is to use real data in combination with synthetic data. Purpose: To improve the performance of pattern recognition systems in computer vision by mixing real and synthetic data for training, and to reduce the time needed for preparing training data. Results: We have built an intelligent information system on the basis of the proposed method which allows the generation of synthetic images. The system allows to generate large and representative samples of images for pattern recognition neural network training. We have also developed software for the synthetic image generator for neural network training. The generator has a modular architecture, which makes it easy to modify, remove or add individual stages to the synthetic image generation pipeline. One can adjust individual parameters (like lighting or blurring) for generated images. The experiment was aimed to compare the accuracy of pattern recognition for a neural network trained on different training samples. The combination of real and synthetic data in model training showed the best recognition performance. Artificially generated training samples, in which the scale of background objects is approximately equal to the scale of the object of interest, and the number of objects of interest in the frame is higher, turned out to be more efficient than other artificially constructed training samples. Changing focal length of the camera in the synthetic image generation scene had no effect on the recognition performance. Practical relevance: The proposed image generation method allows to create a large set of artificially constructed data for training neural networks in pattern recognition in less time than it would take to create the same set of real data.

Spectral invariants of ergodic symbolic systems for pattern recognition and anomaly detection.

Despite tangible advances in machine learning (ML) over the last few decades, many of the ML techniques still suffer from fundamental issues like overfitting and lack of explainability. These issues mandate requirements for mathematical rigor to ensure robust learning from observed data. In this context, topological invariants in data manifolds provide a rich representation of the underlying dynamical system, which can be utilized for developing a mathematically rigorous ML tool to characterize the dynamical behaviour and operational phases of the underlying process. This paper aims to investigate spectral invariants of symbolic systems for detecting changes in topological characteristics of data manifolds. A novel ML approach is proposed, where commutator norms are used on sequences of endomorphisms to symbolically describe dynamical systems on probability spaces with ergodic measures. The objective here is to detect topological invariants of data manifolds that can be used for signal processing, pattern recognition, and anomaly detection. The proposed ML approach is validated on models of selected chaotic dynamical systems for prompt detection of phase transitions. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

A novel way to formalize stable graph cores by using matching-graphs

The increasing amount of data available and the rate at which it is collected leads to rapid developments of systems for intelligent information processing and pattern recognition. Often the underlying data is inherently complex, making it difficult to represent it by linear, vectorial data structures. This is where graphs offer a versatile alternative for formal data representation. Actually, quite an amount of graph-based methods for pattern recognition has been proposed. A considerable part of these methods rely on graph matching. In the present paper, we propose a novel encoding of specific graph matching information. The basic idea is to formalize the stable cores of individual classes of graphs – discovered during intra-class matchings – by means of so called matching-graphs. We evaluate the benefit of these matching-graphs by researching two classification approaches that rely on this novel data structure. The first approach is a distance based classifier focusing on the matching-graphs during dissimilarity computation. For the second approach, we propose to use sets of matching-graphs to embed input graphs into a vector space. The basic idea is to produce hundreds of matching-graphs first, and then represent each graph g as a vector that shows the occurrence of, or the distance to, each matching-graph. In a thorough experimental evaluation on seven real world data sets we empirically confirm that our novel approaches are able to improve the classification accuracy of systems that rely on comparable information as well as state-of-the-art methods.

Multiwavelength Optoelectronic Synapse with 2D Materials for Mixed-Color Pattern Recognition.

Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Conventionally, visual information is captured by an image sensor, stored by memory units, and eventually processed by the machine learning algorithm. Here, we present an optoelectronic synapse device with multifunctional integration of all the processes required for real time object identification. Ultraviolet-visible wavelength-sensitive MoS2 FET channel with infrared sensitive PtTe2/Si gate electrode enables the device to sense, store, and process optical data for a wide range of the electromagnetic spectrum, while maintaining a low dark current. The device exhibits optical stimulation-controlled short-term and long-term potentiation, electrically driven long-term depression, synaptic weight update for multiple wavelengths of light ranging from 300 nm in ultraviolet to 2 μm in infrared. An artificial neural network developed using the extracted weight update parameters of the device can be trained to identify both single wavelength and mixed wavelength patterns. This work demonstrates a device that could potentially be used for realizing a multiwavelength neuromorphic visual system for pattern recognition and object identification.

The Application of Pattern Recognition System in Design Field Based on Aesthetic Principles.

The design system based on aesthetic principles is the most representative in the field of design and has a certain significance for the research and construction of design aesthetics and the development of design education. Therefore, this paper studies the application of pattern recognition system in the field of design based on aesthetic principles and designs a new type of aesthetic principle design system based on pattern recognition in computer vision. This paper proposes pattern similarity measurement and image preprocessing technology to improve the traditional aesthetic principle design system through pattern recognition and then further refine the research of the whole system through histogram equalization and gamma correction. Finally, the MNIST dataset experiment is used to verify the effect of multicolumn convolutional neural network pattern recognition on the aesthetic principle design system. The questionnaire survey experiment in this article and the traditional comparative experiment show that 76% of the public are very satisfied with this design system based on the aesthetic principles of pattern recognition in computer vision. Also, the improved aesthetic principle system scores as high as 90–95 points.

Grey wolf optimization (GWO) with the convolution neural network (CNN)-based pattern recognition system

ABSTRACT The dynamic video frame dataset’s automated feature analysis addresses the complexity of intensity mapping with normal and abnormal classes. Iterative modelling is needed to learn the component of a video frame in several patterns for various video frame data types for threshold-based data clustering and feature analysis. GWO optimises the Convoluted Pattern of Wavelet Transform (CPWT) feature vectors employed in this paper's CNN feature analysis technique. A median filter reduces noise and smooths the video frame before normalising it. Edge information represents the video frame's bright spot boundary. Neural network based video frame classification clusters pixels using feature recurrent learning with minimal dataset training. The filtered video frame's features were evaluated using complex wavelet transformation feature extraction algorithms. These features demonstrate video frame spatial and textural classifications. CNN classifiers help analyse video frame instances and classify action labels. Categorization improves with the fewest training datasets. This strategy may be beneficial if compared to optimal practises.

Review

Deep Learning is-one of the machine learning areas, applied in recent areas. Various techniques have been proposed depends on varieties of learning, including unsupervised, semi-supervised, and supervised-learning. Some of the experimental results proved that the deep learning systems are performed well compared to conventional machine learning systems in image processing, computer vision and pattern recognition. This paper provides a brief survey, beginning with Deep Neural Network (DNN) in Deep Learning area. The survey moves on-the Convolutional Neural Network (CNN) and its architectures, such as LeNet, AlexNet, GoogleNet, VGG16, VGG19, Resnet50 etc. We have included transfer learning by using the CNN’s pre-trained architectures. These architectures are tested with large ImageNet data sets. The deep learning techniques are analyzed with the help of most popular data sets, which are freely available in web. Based on this survey, conclude the performance of the system depends on the GPU system.

Распознавание дефектов поверхностного слоя подшипников на основе фрактального анализа вихретокового сигнала

Bearing manufacturing requires strict adherence to the technological standards and quality control of products at all stages of processing. One of the most accurate and environmentally friendly quality monitoring methods in bearing production is the eddy current method. When using this method, a large array of numerical data is generated that reflects information on the physical and mechanical conditions of the surface. Thus, defect detection process is reduced to defining the task of data segmentation, that is, selection of parts of an array with different dynamics, which is rather difficult for an operator. To solve this problem, a pattern recognition system is proposed, the elements &#x0D; of which are an alphabet of classes (defects of the surface layer), a dictionary of features (fractal dimension of a data array), rules for decision making on recognition (method of standards). Fractal analysis is applied to a two-dimensional array consisting of several data vectors, which will increase the degree of automation of the process &#x0D; of searching and identifying sensor readings indicating a defect, since the defect affects the surface layer of the product not only in the area of its greatest prevalence. Three methods of calculating the fractal dimension of a two-dimensional array are presented. Application of the reference method of the theory of pattern recognition is substantiated, an analytical expression that reflects the method is presented. According to the proposed solutions, it is possible to classify the defect of the rolling surface of the bearing rings by preliminary calculating the fractal dimension for each defect. Implementation of the software developed by using this method can improve the efficiency of quality control in bearing manufacturing and update the technological process.

On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules.

Microhaplotype and Y-SNP/STR (MY): A novel MPS-based system for genotype pattern recognition in two-person DNA mixtures

BackgroundsY-chromosomal haplotypes based on Y-short tandem repeats (STRs) and Y-single nucleotide polymorphisms/insertion and deletion polymorphisms (SNPs/InDels) are used to characterize paternal lineages of unknown male trace donors. However, Y-chromosomal genetic markers are not currently sufficient for precise individual identification. Microhaplotype (MH), generally < 200 bp on autosomes and consisting of two or more SNPs, was recently introduced in forensic genetics with the development of massive parallel sequencing technology and may facilitate identification and DNA mixture deconvolution. Therefore, combining the two kinds of genetic markers may be beneficial in many forensic scenarios, especially crime scenes with male suspects, such as sexual assault cases. MethodsIn the present study, we developed a novel MPS-based panel, Microhaplotype and Y-SNP/STR (MY), by multiplex PCR and 150-bp paired-end sequencing, including 114 Y-SNPs (twelve dominant Y-DNA haplogroups), 45 Y-STRs (N-1 stutter < 0.09; estimated mutation rate < 5 × 10−3), and 22 MHs (allele coverage ratio > 0.91; pairwise distance > 10 Mb). Additionally, MY system-based genotype pattern recognition (GPR), a regression-based method to identify the genotype pattern for each MH locus, is proposed for two-person DNA mixture deconvolution. We integrated 26 two-person genotype combinations into nine genotype patterns and validated the application range of GPR based on DNA profiles of ten sets of simulated male-male DNA mixtures (1:10–1:2). ResultsThe effective number of alleles (Ae) ranged from 3.62 to 14.72, with an average of 7.17, in 100 Chinese Guangdong Han individuals. The cumulative discrimination power was 1–5.00 × 10−31, and the cumulative power of exclusion was 1–5.00 × 10−8 and 1–4.85 × 10−12 for duo and trio paternity testing, respectively. Furthermore, the actual mixing ratio-depth of coverage (DoC) ratio (RDoC) regression relationships were established for different genetic markers and genotype patterns. In five overlapping areas, genotype differentiation of the major and minor contributors required likelihood ratio methods. In nonoverlapping areas, the genotype pattern could be recognized by comparing the observed RDoC and RDoC ranges. ConclusionThe GPR can be used to deconvolute two-person DNA mixtures (application range: 1:10–1:2) for individual identification.

Generation of Random Fields for Image Segmentation Techniques: A Review

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs.

Rejecting Novel Motions in High-Density Myoelectric Pattern Recognition Using Hybrid Neural Networks.

The objective of this study is to develop a method for alleviating a novel pattern interference toward achieving a robust myoelectric pattern-recognition control system. To this end, a framework was presented for surface electromyogram (sEMG) pattern classification and novelty detection using hybrid neural networks, i.e., a convolutional neural network (CNN) and autoencoder networks. In the framework, the CNN was first used to extract spatio-temporal information conveyed in the sEMG data recorded via high-density (HD) 2-dimensional electrode arrays. Given the target motion patterns well-characterized by the CNN, autoencoder networks were applied to learn variable correlation in the spatio-temporal information, where samples from any novel pattern appeared to be significantly different from those from target patterns. Therefore, it was straightforward to discriminate and then reject the novel motion interferences identified as untargeted and unlearned patterns. The performance of the proposed method was evaluated with HD-sEMG data recorded by two 8 × 6 electrode arrays placed over the forearm extensors and flexors of 9 subjects performing seven target motion tasks and six novel motion tasks. The proposed method achieved high accuracies over 95% for identifying and rejecting novel motion tasks, and it outperformed conventional methods with statistical significance (p < 0.05). The proposed method is demonstrated to be a promising solution for rejecting novel motion interferences, which are ubiquitous in myoelectric control. This study will enhance the robustness of the myoelectric control system against novelty interference.

An SVM-GA based monitoring system for pattern recognition of autocorrelated processes

An SVM-GA based monitoring system for pattern recognition of autocorrelated processes