Pattern Recognition Applications Research Articles

An improved belief $$\chi ^2$$ divergence for Dempster–Shafer theory and its applications in pattern recognition

An improved belief $$\chi ^2$$ divergence for Dempster–Shafer theory and its applications in pattern recognition

A belief Rényi divergence for multi-source information fusion and its application in pattern recognition

A belief Rényi divergence for multi-source information fusion and its application in pattern recognition

Similarity Measures based on the Novel Interval-valued Picture Hesitant Fuzzy Sets and their Applications in Pattern Recognition

Similarity Measures based on the Novel Interval-valued Picture Hesitant Fuzzy Sets and their Applications in Pattern Recognition

A 3D Distance Measure for Intuitionistic Fuzzy Sets and its Application in Pattern Recognition and Decision-Making Problems

The distance measure as an information measure helps in processing incomplete and confusing data to arrive at a conclusion by assessing the degree of difference between pairs of variables. Reviewing distance measures for Intuitionistic Fuzzy Sets (IFSs), we have pointed out several drawbacks of the existing measures. To overcome these, this paper presents a new distance measure between IFSs based on the probabilistic divergence measure. Several mathematical properties of the proposed metric are established and validated via numerical examples. This proposed definition is further used to devise several similarity measures. Applicability and consistency of the introduced measures have been corroborated by various examples. In addition to that, rationality of the proposed metric is established by applying it to pattern recognition applications, Multi-Attribute-Decision-Making (MADM) problems and medical & pathological diagnoses. Analysis of the results establishes that the suggested measure overcomes shortcomings associated with existing measures and thereby authenticates the superiority of the proposed measure.

Off-line English character recognition system

Purpose of the study: This paper aims to recognition of handwritten English characters in offline mode. It develops an efficient character recognition model avoiding large variations in handwriting by using better feature extraction techniques. Methodology: The samples of characters are preprocessed by applying a sequence of operations in succession like Thickening, Thresholding, Filtering, and Thinning. Efficient features like Gradient features and Zonal features have been extracted. Gradient features are helpful to find out stroke information in the character whereas Zonal features detail out local information in a more précised way. Hidden Markov Model is the classifier. Main Findings: Classification has been started with only a 5-state HMM model but it is observed that as the number of states of HMM model is increased, the corresponding recognition rate is also improved. Finally, with the 36 states HMM model we have got the expected result. This produces an overall average recognition rate of 92.6%. For the letters ‘A’ and ‘W’, the recognition rate is found to be very low, because of a lot of variations in writing style of these letters. Applications of this study: HMM is a flexible tool which is capable of absorbing variations in character images. The future works will be concentrated on improvement of recognition rate of such letters by finding some demarcating features and post processing. The proposed method can be well used in Natural Language Processing, Signature verification, Face recognition like other Pattern Recognition applications. Novelty/Originality of this study: Preprocessing uses Median filter which removes all stray marks in samples and hence avoids any possibility of false pixels. The combination of Gradient features and Zonal features leads to a recognition accuracy of 92.6% which may be used by researchers in any other domains for the purpose of classification. The application of HMM will motivate the readers to use it for better results of classification.

Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set

The success of many computer vision and pattern recognition applications depends on matching local features on two or more images. Because the initial correspondence set—i.e., the set of the initial feature pairs—is often contaminated by mismatches, removing mismatches is a necessary task prior to image matching. In this paper, we first propose a fast geometry histogram-based (GH-based) mismatch removal strategy to construct a reduced correspondence set Creduced,GH from the initial correspondence set Cini. Next, we propose an effective cooperative random sample consensus (COOSAC) method for remote sensing image matching. COOSAC consists of a RANSAC, called RANSACini working on Cini, and a tiny RANSAC, called RANSACtiny,GH working on a randomly selected subset of Creduced,GH. In RANSACtiny,GH, an iterative area constraint-based sampling strategy is proposed to estimate the model solution of Ctiny,GH until the specified confidence level is reached, and then RANSACini utilizes the estimated model solution of Ctiny,GH to calculate the inlier rate of Cini. COOSAC repeats the above cooperation between RANSACtiny,GH and RANSACini until the specified confidence level is reached, reporting the resultant model solution of Cini. For convenience, our image matching method is called the GH-COOSAC method. Based on several testing datasets, thorough experimental results demonstrate that the proposed GH-COOSAC method achieves lower computational cost and higher matching accuracy benefits when compared with the state-of-the-art image matching methods.

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

In order to solve the problem that the traditional detection technology can not meet the requirements of online detection, a visual detection device for bearing inner ring defects based on image processing and pattern recognition is proposed in this paper. The device systematically designs an image acquisition device of bearing inner ring based on CCD. In the hardware scheme, the appropriate lens, camera, light source, and other related hardware are selected according to the actual needs, a complete image acquisition platform of bearing inner ring is built, and the software function design is completed. The simulation results show that the qualified rate of machine detection is 98.6%, the missed detection rate is 0, and the false detection rate is 1.4%, which are better than manual detection. Conclusion. The test results show that the bearing inner ring defect detection system can detect the surface defects of bearing inner ring quickly, stably, and reliably, and the detection efficiency and accuracy are higher than the original manual detection method, so it has a good application prospect.

Local Lagrange Exponential Stability Analysis of Quaternion-Valued Neural Networks with Time Delays

This study on the local stability of quaternion-valued neural networks is of great significance to the application of associative memory and pattern recognition. In the research, we study local Lagrange exponential stability of quaternion-valued neural networks with time delays. By separating the quaternion-valued neural networks into a real part and three imaginary parts, separating the quaternion field into 34n subregions, and using the intermediate value theorem, sufficient conditions are proposed to ensure quaternion-valued neural networks have 34n equilibrium points. According to the Halanay inequality, the conditions for the existence of 24n local Lagrange exponentially stable equilibria of quaternion-valued neural networks are established. The obtained stability results improve and extend the existing ones. Under the same conditions, quaternion-valued neural networks have more stable equilibrium points than complex-valued neural networks and real-valued neural networks. The validity of the theoretical results were verified by an example.

New Deep Spatio-Structural Features of Handwritten Text Lines for Document Age Classification

Document age estimation using handwritten text line images is useful for several pattern recognition and artificial intelligence applications such as forged signature verification, writer identification, gender identification, personality traits identification, and fraudulent document identification. This paper presents a novel method for document age classification at the text line level. For segmenting text lines from handwritten document images, the wavelet decomposition is used in a novel way. We explore multiple levels of wavelet decomposition, which introduce blur as the number of levels increases for detecting word components. The detected components are then used for a direction guided-driven growing approach with linearity, and nonlinearity criteria for segmenting text lines. For classification of text line images of different ages, inspired by the observation that, as the age of a document increases, the quality of its image degrades, the proposed method extracts the structural, contrast, and spatial features to study degradations at different wavelet decomposition levels. The specific advantages of DenseNet, namely, strong feature propagation, mitigation of the vanishing gradient problem, reuse of features, and the reduction of the number of parameters motivated us to use DenseNet121 along with a Multi-layer Perceptron (MLP) for the classification of text lines of different ages by feeding features and the original image as input. To demonstrate the efficacy of the proposed model, experiments were conducted on our own as well as standard datasets for both text line segmentation and document age classification. The results show that the proposed method outperforms the existing methods for text line segmentation in terms of precision, recall, F-measure, and document age classification in terms of average classification rate.

Graph representations for the analysis of multi-agent spatiotemporal sports data

Analyzing tactical patterns in invasion games using multi-agent spatiotemporal data is a challenging task at the intersection of computer and sports science. A fundamental yet understudied problem in this area is finding an optimal data representation for processing athlete trajectories using machine learning algorithms. In the present work, we address this gap by discussing common representations in use and propose Tactical Graphs, an alternative graph-based format capable of producing integrative, contextualized models for machine learning applications. We provide an in-depth, domain-specific motivation of the proposed data representation scheme and show how this approach exploits inherent data traits. We propose Tactical Graph Networks (TGNets), a light-weight, hybrid machine learning architecture sensitive to player interactions. Our method is evaluated with an extensive ablation study and the first comprehensive state of the art comparison between standard feature, state vector, and image-based methods on the same dataset. Experiments were conducted using real-world football data containing short sequences of defensive play labelled according to the outcome of ball winning attempts. The results indicate that TGNets are on par with state-of-the-art deep learning models while exhibiting only a fraction of their complexity. We further demonstrate that selecting the right data representation is crucial as it has a significant influence on model performance. The theoretical findings and the proposed method provide insights and a strong methodological alternative for all classification, prediction or pattern recognition applications in the areas of collective movement analysis, automated match analysis, and performance analysis.

Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation

Domain adaptation (DA) is widely used in computer vision and pattern recognition applications. It is an effective process where a model is trained on objects from the source domain to predict the categories of the objects in the target domain. The aim of feature extraction in domain adaptation is to learn the best representation of the data in a certain domain and use it in other domains. However, the main challenge here is the difference between the data distributions of the source and target domains. Also, in computer vision, the data are represented as tensor objects such as 3-D images and video sequences. Most of the existing methods in DA apply vectorization to the data, which leads to information loss due to failure to preserve the natural tensor structure in a low-dimensional space. Thus, in this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised DA-based tensor feature learning (UDA-TFL)</i> as a novel adapted feature extraction method that aims to avoid vectorization during transfer knowledge simultaneously; retain the structure of the tensor objects; reduce the data discrepancy between source and target domains; and represent the original tensor object in a lower dimensional space that is resistant to noise. Therefore, multilinear projections are determined to learn the tensor subspace without vectorizing the original tensor objects via an alternating optimization strategy. We integrate maximum mean discrepancy in the objective function to reduce the difference between source and target distributions. Extensive experiments are conducted on 39 cross-domain datasets from different fields, including images and videos. The promising results indicate that UDA-TFL significantly outperforms the state-of-the-art.

The Detection of Activities Occurring Inside Quick Service Restaurants That Influence Air Quality.

Our attention was focused on the identification of activities affecting air quality, which occur in quick-service restaurants (QSR). The work was based on a measurement study of selected kebab stores in the Polish city of Wrocław. It demonstrated that activities taking place in kebabs altered air quality. The associated changes in air parameters such as temperature, relative humidity, CO2 concentration, and the content of volatile organic compounds could be detected by utilizing a simple, multi-sensor device. In the measurement data, there were identified multidimensional patterns, which proved to be specific for the following categories of activities: Night Hours, Outlet Preparation, Food Preparation, Operation Hours, and Cleaning. Their occurrence was recognized by pattern recognition methods with a true positive rate greater than 99%. We demonstrated that the recognition may be based on measurements performed in various locations within the kebab store. Although patterns of the individual categories of activities largely varied between kebab stores, a similar performance of recognition was achieved for all restaurants. The obtained results entitled us to conclude that it is possible to detect activities of QSR, which influenced air quality, with the application of sensor technique and pattern recognition. The proposed approach may be applied to this type of object in general.

OCR for Arabic character recognition with advanced principal component analysis based on feature extraction and fuzzy-KNN based classification

Offline character recognition has become a highly important study field for various pattern recognition applications in recent years. Several handwritten character recognition systems have been suggested, with the complexity of these systems varying depending on the recognizing units' writing styles. In reality, identifying letters or numerals is far simpler than recognizing cursive sentences or lines of text. As a result, early handwriting recognition algorithms could only distinguish a few characters with limited vocabularies. Nowadays Arabic handwritten character recognition is very important as it is very difficult to identify. The cursive writing and variety of styles make this recognition more complex. This paper presents an automated model for ACR. This ACR is constructed from four phases: Preprocessing, Segmentation, Feature Extraction, and Classification. In this research article, we compare the advanced EPSO EKNN Algorithm with the earlier DBN Algorithm and also suggest a new method having higher Accuracy. In this research article, the "Enhanced K-Nearest Neighbor" (EKNN) classification model was used to identify and classify or simply recognize the particular Arabic character, and the "Extended Particle Swarm Optimization" (EPSO) methodology was introduced to select the best feature from feature extraction to comply with Arabic-Character Classification.

Application of the Signal Spectrum-Based Image Pattern Recognition in the Vibration Process Analysis

One of the aims of vibration signal analysis is the assessment of the condition of technical objects at each stage of operation based on a cause-and-effect relationship between the technical condition of the device and the features of the vibration signal available through the measurement experiment. The essential features of the signal (in the form of certain measures) are available through appropriate operations on the measurement signal. The problem arises when the measuring signal is not a fully deterministic one, it shows features common to deterministic and random signals. Consequently, some simple periodic signal measures such as spectrum frequency components introduce some level of uncertainty in the before mentioned cause-and-effect relationship. Thus, the assessment of the technical condition in the selected frequency components of the spectrum of the measuring signal may not be reliable. These difficulties can be overcome by using the frequency-frequency analysis of the diagnostic signal. The entire frequency spectrum is converted into an image in which the frequency components form characteristic patterns unique to the actual condition of the technical object. The final assessment of the condition is achieved through the identification of a unique image pattern by comparison with reference image patterns created with a generation model.

Polygonal approximation for digital image boundary using firefly algorithm

Polygonal approximation (PA) is a challenging problem and has its wide applications in computer vision, electronic imaging, image classification, image visualization, pattern recognition and image analysis. This paper proposes a stochastic technique-based firefly algorithm (FA) for PA. FA customizes a kind of randomization by searching a set of solutions where PA requires more combinations of approximation to find an optimal solution. The significant feature of the FA is real random sample generation and attractive features. The attractiveness and brightness of the firefly have been used efficiently to solve the approximation problem by detecting the dominant points (DPs) with fewer iterations to produce better results. Thus, this technique achieves the main goal of PA that is a minimum error value with a lesser number of DPs. While compared with other similar algorithms, FA is independent of velocities which are considered as an advantage for this algorithm. Subsequently, the multiswarm nature of FA allows finding multiple optimal solutions concurrently. The experimental results show that the proposed algorithm generates better solutions compared with other algorithms.

Wireless modulation classification based on Radon transform and convolutional neural networks

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions.

Adaptive infrared and visible image fusion method by using rolling guidance filter and saliency detection

Infrared and visible images have good complementarity; thus, infrared and visible image fusion methods are extensively used in military, target detection, pattern recognition, and other applications. However, problems such as halo effect, poor background texture, and contrast reduction are encountered in image fusion methods. In addition, when the visible image is disturbed by smoke and high brightness, it will affect the quality of the fused image, making image fusion a challenge. To address these problems, an adaptive image fusion method is proposed in this paper. First, the image decomposition method based on rolling guidance filter and saliency detection (RGFSD) is proposed. Next, the source image is decomposed into three layers by using RGFSD: detail layer, salient layer, and base layer. The detail layer is then fused using the phase congruency strategy. Subsequently, the local entropy and gradient (LEG) method is proposed to simulate the perception of significant information by the human visual system to assign the weights for salient layer fusion. Next, the base layer is fused using the averaging method. Furthermore, an image enhancement method based on rolling guidance filter and contrast-limited adaptive histogram equalization is proposed to enhance the detail and contrast of the fused image. Finally, the proposed method is compared qualitatively and quantitatively with eight fusion methods. The experiment results show that compared with the existing methods, the proposed method can better enhance the contrast and retain details of the source images.

Editorial of the Special Issue “Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity”

Due to the significant increase in the availability of new data in recent years, as a result of the expansion of available seismic stations, laboratory experiments, and the availability of increasingly reliable synthetic catalogs, considerable progress has been made in understanding the spatiotemporal properties of earthquakes [...]

Intuitionistic Fuzzy Similarity-Based Information Measure in the Application of Pattern Recognition and Clustering

Intuitionistic Fuzzy Similarity-Based Information Measure in the Application of Pattern Recognition and Clustering