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License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others. [ABSTRACT FROM AUTHOR]

Head pose estimation is an especially challenging task due to the complexity nonlinear mapping from 2D feature space to 3D pose space. To address the above issue, this paper presents a novel and efficient head pose estimation framework based on particle swarm optimized contrastive learning and multimodal entangled graph convolution network. Firstly, a new network, the region and difference‐aware feature pyramid network (RD‐FPN), is proposed for 2D keypoints detection to alleviate the background interference and enhance the feature expressiveness. Then, particle swarm optimized contrastive learning is constructed to alternatively match 2D and 3D keypoints, which takes the multimodal keypoints matching accuracy as the optimization objective, while considering the similarity of cross‐modal positive and negative sample pairs from contrastive learning as a local contrastive constraint. Finally, multimodal entangled graph convolution network is designed to enhance the ability of establishing geometric relationships between keypoints and head pose angles based on second‐order bilinear attention, in which point‐edge attention is introduced to improve the representation of geometric features between multimodal keypoints. Compared with other methods, the average error of our method is reduced by 8.23%, indicating the accuracy, generalization, and efficiency of our method on the 300W‐LP, AFLW2000, BIWI datasets. [ABSTRACT FROM AUTHOR]

This paper delves into the expansive world of cellular automata (CA), abstract models of computation comprised of cells that interact based on predefined rules. Originating from John von Neumann's work in the 1940s, CA has evolved into a multidisciplinary field with applications ranging from mathematical concepts to complex simulations of biological, physical, computer science, material science, and social systems. The paper reviews its historical development, emphasizing John Conway's influential Game of Life and Burk's seminar collection. The authors categorize and explore a myriad of CA topics, including self-replicating automata, the universality of computation, compromises in CA, variants, applications in biological systems, fault-tolerant computation, pattern recognition, CA games, fractals, dynamic properties, complexity, image processing, cryptography, bioinformatics, materials modeling, probabilistic automata, and contemporary research. The significance of cellular automata for materials modeling cannot be overstated and considerable attention has been devoted to the issues of modeling nucleation and recrystallization. The review aims to provide a comprehensive resource for both beginners and experts in the field, shedding light on cellular automata's dynamic and diverse applications in various aspects of life and scientific inquiry. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. [ABSTRACT FROM AUTHOR]

Existing segmentation‐based methods have made considerable progress in arbitrarily shaped text detection due to the advantage of dealing with shape variation. However, there still exist challenges to detecting accurate text instances with dense layouts, inaccurate annotations, and complex backgrounds. Many recent works have focused on improving arbitrary boundary prediction, but it may be difficult to accurately distinguish each instance of dense layouts because their boundary pixels may be mistakenly classified to produce inaccurate results (i.e., adhesive texts) with inaccurate annotation and complex backgrounds. Considering the local and long‐range dependencies, this paper proposes an efficient text detector, namely T‐Skeleton, to obtain more reliable segmentation detections. In the spirit of object skeletonization, we introduce the text instance skeleton highlighting the semantically significant structure (similar to the skeleton of a fish) to explicitly capture the long‐range dependencies of text instances. The key idea of T‐Skeleton is to calibrate the coarse text proposals by embedding text instance skeletons to separate crowd texts accurately and robustly. We further design a channel attention module to enlarge the performance margin between T‐Skeleton and the segmentation baseline. Experimental results on four publicly available datasets show the superiority of T‐Skeleton in handling long and curved texts. [ABSTRACT FROM AUTHOR]

Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors' scheme, which achieves superior performance. [ABSTRACT FROM AUTHOR]

Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications. [ABSTRACT FROM AUTHOR]

The paper presents some improvements of fast contour recognition algorithm. The main idea of this algorithm is based on calculation of geometric characteristics of a two-dimensional surface in , which encodes digital images. To obtain the contours, Minkowski fractal dimension calculation is used. A postprocessing algorithm of contour thinning without loss of quality of entire contour picture is proposed in the paper. An estimate of the speed of the new algorithm is presented in comparison with the well-known implementation of the Canny algorithm using the OpenCV computer vision library and the parallel architecture of CUDA. Examples demonstrating the efficiency of the new algorithm are presented. [ABSTRACT FROM AUTHOR]

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body. [ABSTRACT FROM AUTHOR]

Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning. [ABSTRACT FROM AUTHOR]

Recently, several arbitrary-scale models have been proposed for single-image super-resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is emphasized for applications such as satellite image processing, HR display, and video-based surveillance. However, the baseline integer-scale model must be retrained to fit the existing network, and the learning speed is slow. This paper proposes a network to solve these problems, processing super-resolution by restoring the high-frequency information lost in the remaining arbitrary-scale while maintaining the baseline integer scale. The proposed network extends an integer-scaled image to an arbitrary-scale target in the discrete cosine transform spectral domain. We also modulate the high-frequency restoration weights of the depthwise multi-head attention to use memory efficiently. Finally, we demonstrate the performance through experiments with existing state-of-the-art models and their flexibility through integration with existing integer-scale models in terms of peak signal-to-noise ratio (PSNR) and similarity index measure (SSIM) scores. This means that the proposed network restores high-resolution (HR) images appropriately by improving the image sharpness of low-resolution (LR) images. [ABSTRACT FROM AUTHOR]

Spatial failure patterns in wafer defect maps are very useful for root cause analysis, which is essential for yield optimization in the manufacturing process. Especially, the scratch defect type is the most challenging pattern to recognize because the position, shape, size and curvature vary widely from one scratch to another. Discontinuity points within scratches also contribute to the low recognition rate, and such points are often hidden defective dies that become reliability threat. Previous studies on defect pattern identification show that the recognition rate for scratches is among the lowest in all patterns even if the overall accuracy is high. In this paper, we propose a novel scratch pattern recognition method. In this paper, image processing techniques are applied to recognize scratch patterns in wafer maps. The Hough transform is first employed to identify line segments, from which scratches can be reconstructed. In contrast to previous machine-learning based methods, there is no need to train a complicated prediction model so that the computation time is small. The method is validated by using wafer maps from six real products and public data. Experimental results show that the proposed method achieves high recall, precision and accuracy in all cases. [ABSTRACT FROM AUTHOR]

License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge detection techniques, along with the bounding box method for identifying and revealing license plate characters while removing unwanted artifacts such as dust and fog. The mathematical model of foggy images is presented and the sum of gradients of the image, which represents the visibility of the image, is improved. Previous works on license plate recognition have utilized non-intelligent pattern matching techniques. The proposed technique can be applied in a variety of settings, including traffic monitoring, parking management, and law enforcement, among others. The applied algorithm, unlike SOTA-based methods, does not need a huge set of training data and is implemented only by applying standard templates. The main advantages of the proposed algorithm are the lack of a need for a training set, the high speed of the training process, the ability to respond to different standards, the high response speed, and higher accuracy compared to similar tasks. [ABSTRACT FROM AUTHOR]

Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. [ABSTRACT FROM AUTHOR]

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does. [ABSTRACT FROM AUTHOR]

Considering the defect of object recognition with single global feature or local feature, in this letter the authors propose a shape interest points descriptor (SIPD) for object recognition. Particularly, the authors extract the shape features by the improved HU moments and then interest points by Speeded up Robust Feature (SURF). Object recognition is carried out by similarity measure. Because the influence of the improved HU moments and SURF for object recognition is different, the two different similarity measures are fused effectively by using different weight factors. Experimental results show that the author' proposed method is effective and robust to the changes of scale, viewing angle, illumination and noise when compared with the other representative methods. [ABSTRACT FROM AUTHOR]

This paper proposes a color, scale, and rotation independent approach for recognizing License Plate (LP) characters written in English. We extract features based on geometrical properties of an LP character, like the number of perimeters, perimeter angles, different characteristics points on the perimeter, etc. These features are next used to generate a characteristic encoding of the character. The generated encoding based on color, scale, and rotation independent geometrical properties is then used to identify an LP character independent of its color, scale, and rotational angle. We have evaluated our proposed approach using images from the publicly available Media Lab LP dataset, and a recognition rate of 98.29% was obtained. The processing time for an input image of size $ 200 \times 100 $ 200 × 100 pixel was found to be 0.3 ms. The recognition rate and low processing time compare favorably with results published in the literature by other LP characters recognition techniques. Further, unlike other published techniques, our approach places no restriction on the size, color, or the number of characters in LP, nor is our method restricted to any particular LP design or the LP of any specific country or region. [ABSTRACT FROM AUTHOR]

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day. [ABSTRACT FROM AUTHOR]

The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occur-rences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors' data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices. [ABSTRACT FROM AUTHOR]

Many image processing and pattern recognition problems can be formulated as binary quadratic programming (BQP) problems. However, solving a large BQP problem with a good quality solution and low computational time is still a challenging unsolved problem. Current methodologies either adopt an independent random search in a semi-definite space or perform search in a relaxed biconvex space. However, the independent search has great computation cost as many different trials are needed to get a good solution. The biconvex search only searches the solution in a local convex ball, which can be a local optimal solution. In this paper, we propose a BQP solver that alternatingly applies a deterministic search and a stochastic neighborhood search. The deterministic search iteratively improves the solution quality until it satisfies the KKT optimality conditions. The stochastic search performs bootstrapping sampling to the objective function constructed from the potential solution to find a stochastic neighborhood vector. These two steps are repeated until the obtained solution is better than many of its stochastic neighborhood vectors. We compare the proposed solver with several state-of-the-art methods for a range of image processing and pattern recognition problems. Experimental results showed that the proposed solver not only outperformed them in solution quality but also with the lowest computational complexity. [ABSTRACT FROM AUTHOR]

In this paper an example of Advance Driving Assistance Systems (ADAS) from automotive are implemented on a small-scale vehicle. The ADAS technologies reefer to integration of multiple electronic systems of sensors and microcontrollers for detecting nearby obstacles or driver errors and respond accordingly. Several functions of ADAS uses cameras to record external environmental images for traffic signs recognition and lane assist. For implementing the above functionalities, modern software techniques are considered for processing the data acquired by the Raspberry camera on the mobile platform. Deep learning and image processing algorithms have been interconnected in a Raspberry Pi controller on the vehicle. Software development and implementation of the above algorithms have been carried out with Phyton programing language. The proposed algorithm is used to calculate the angle of turn required to stay in the current lane while reading the traffic signs for adapting the speed. Results and conclusions are presented at the end of this article. [ABSTRACT FROM AUTHOR]

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

The relevance of the topic, in particular, if to take one of the information flows, whether it is the action of a human factor or a specific object, then it is true that special processing of the machine learning language and automatic information output significantly optimize human life. With the help of neural networks and their chest radiography is one of the most accessible radiological studies for screening and diagnosis of many lung diseases a special machine learning language is to study the flow of information about it and the same object in real time using neural networks. The article describes the terminology of the problem of X-ray recognition using machine learning methods and algorithms, examines the relevance of the problem, and analyzes the current state of the problem in the field of X-ray recognition. The aspects of the problem being solved, identified during the analysis, in the form of solved problems, approaches, methods, information technologies used, tools and software solutions to the problem are noted The paper is devoted to the description of a modified method of fuzzy clustering of halftone images, which at each iteration performs a dynamic transformation of the source data based on a singular decomposition with automatic selection of the most significant columns of the matrix of left singular vectors. The results of experimental studies were obtained by processing X-ray images. As a result of testing a neural network model, in the output layer of which a sigmoidal activation function was used to activate neurons, and an algorithm was used as an optimization method, the best values of accuracy and completeness were obtained: accuracy – 94.2 During testing, the neural network showed an accuracy of pneumonia recognition equal to 94,27 %. [ABSTRACT FROM AUTHOR]

In ceramic tiles manufacturing industry, tiles are manufactured at large scale which makes it more challenging to ensure the quality of each tile according to the set standards. Mostly, Statistical Process Control (SPC) is used by tile manufacturers at each step to monitor various processing parameters. SPC procedures are implemented manually that requires sufficient number of experienced human resource to identify defected tiles from a batch of tiles. The manual inspection also gives low accuracy of defect detection due to human errors and hard environment. Considering these drawbacks, in this paper an automated defect detection method is proposed which is based on image processing and morphological operation to ensure the quality and standard of tiles. The proposed method resizes and converts the input RGB image into grayscale image and removes any possible noisy artifacts. An edge detection algorithm is applied on grayscale image to enhance the edges representing the cracks. Afterwards, morphological erosion and dilation operations are applied, one at a time, to get two intermediate images. Finally, edges are detected by subtracting eroded intermediate image from dilated intermediate image. For detection, the proposed algorithm does not require any separate reference image. The algorithm is tested on an image set of sixty different defected tile images and attained 92% average detection accuracy. [ABSTRACT FROM AUTHOR]

Abstract Existing segmentation‐based methods have made considerable progress in arbitrarily shaped text detection due to the advantage of dealing with shape variation. However, there still exist challenges to detecting accurate text instances with dense layouts, inaccurate annotations, and complex backgrounds. Many recent works have focused on improving arbitrary boundary prediction, but it may be difficult to accurately distinguish each instance of dense layouts because their boundary pixels may be mistakenly classified to produce inaccurate results (i.e., adhesive texts) with inaccurate annotation and complex backgrounds. Considering the local and long‐range dependencies, this paper proposes an efficient text detector, namely T‐Skeleton, to obtain more reliable segmentation detections. In the spirit of object skeletonization, we introduce the text instance skeleton highlighting the semantically significant structure (similar to the skeleton of a fish) to explicitly capture the long‐range dependencies of text instances. The key idea of T‐Skeleton is to calibrate the coarse text proposals by embedding text instance skeletons to separate crowd texts accurately and robustly. We further design a channel attention module to enlarge the performance margin between T‐Skeleton and the segmentation baseline. Experimental results on four publicly available datasets show the superiority of T‐Skeleton in handling long and curved texts.

Abstract Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors’ scheme, which achieves superior performance.

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed. [ABSTRACT FROM AUTHOR]

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a comparison between three multimodal biometric systems used to authenticate human by fusion of palm dorsal hand and finger veins which used alternately. We developed an image analysis technique to extract region of interest (ROI) from palm dorsal hand and finger veins image. After extracting ROI we design a sequence of preprocessing steps to improve palm, dorsal hand and finger veins images using Homomorphic, Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart systems are based on the following algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (KNN) classifier for matching operation. The database chosen was the CASIA Multi-Spectral Palm print Image Database V1.0,Bosphorus Hand Vein Database and the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT). The achieved results for the fusion biometric traits were Correct Recognition Rate (CRR) finger and dorsal hand veins is 96.8%., palm and dorsal hand veins is 97.6%, palm and finger veins is 97.9% [ABSTRACT FROM AUTHOR]

Abstract License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M.

Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions. [ABSTRACT FROM AUTHOR]

Robot-assisted Medical Ultrasound Imaging Systems (RMUIS) leverage the accuracy and stability of robotic motion and offer the potential to standardize medical ultrasound imaging. RMUIS expand the scope of medical ultrasound applications and promote ultrasound in a wider range of application scenarios and imaging modalities. Compared to free-hand ultrasound, RMUIS can interpret image information more quantitatively and acquire stable images over a long period of time. This not only reduces the operator's workload, but also provides better quantitative results in elastography and 3D imaging. In addition, RMUIS can be better combined with interventional treatment, such as percutaneous biopsy, radiofrequency ablation, thrombolytic therapy, etc. However, autonomous RMUIS and other applications are still in the research phase. Insufficient interactivity, effectiveness, and safety prevent RMUIS from large-scale clinical application. There is an urgent need for intelligent learning to improve the capabilities of RMUIS. In addition, RMUIS is a multidisciplinary system of software and hardware. Hardware modifications and upgrades are also important. In this paper, we review the state of the art of RMUIS and summarize the key technical topics. In each topic, we present a variety of system composition options according to the classification of hardware. A variety of software and algorithms based on different hardware are also presented. We then present the applications of RMUIS in healthcare, showing how the capabilities of robots can be used to improve healthcare. Finally, we summarize the current challenges of RMUIS and propose future directions to explore more diverse applications and promote the practical application of RMUIS in medical scenarios. [ABSTRACT FROM AUTHOR]

• The purpose of this paper is to identify the timely failure due to wear in rails. • The image processing techniques is used for wear detection in rails. • This provides a methodological basis for quantitative indicators of wear. • Comparing the results from image processing method is in accordance with field observations. • The accuracy of the proposed method indicates the validity of the proposed model. The purpose of controlling and detecting errors in transportation systems is to ensure the correct operation of the system. The purpose of this paper is to identify wear in rails by using statistical pattern recognition methods. Due to the low accuracy of some of the current methods designing and introducing an easy way to be used in this area is required. The data of this paper were obtained by the field observation and using image captions of without worn profile and worn profile of railway rails, then the wear phenomenon was identified using the image processing method. Comparing the results of wear identification using pattern recognition techniques is in accordance with field observations. The true positive rate (sensitivity) is calculated and the accuracy of the proposed method indicates the validity of the proposed model. [ABSTRACT FROM AUTHOR]

The Cochin Port physical tidal model available at Central Water and Power Research Station (CWPRS), Ministry of Jal Shakti, Government of India, is used to simulate the tidal conditions of real-time Cochin Port. Analysis of physical tidal model takes time and requires specialised knowledge in order to analyse and identify the velocity profile through manual instrumentation. The use of artificial intelligence (AI) based image processing techniques may reduce the analysis time and improve the accuracy of physical tidal model. An attempt was made to determine the velocity profile of Cochin Port model (scaled down model) at CWPRS through AI-based video image-processing analysis using MATLAB. Based on the analysis, it was observed that magnitude of horizontal velocity is varying for 1579 times in about 30.36 sec duration from 0.01156 m/s to 0.32986 m/s with a mean velocity magnitude of about 0.20357 m/s. The results indicated that digital videography-based image processing approach with the assistance of trained AI algorithms gives the précised velocity profile having 578 unique magnitude variations out of 1579 readings in 30.36 sec, which is far better than the manual instrumentation, and therefore, manual instrumentation may be substituted with the developed artificial intelligence (AI) based image processing technique. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann–Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up. [ABSTRACT FROM AUTHOR]

Morris water maze (MWM) test is widely used to evaluate the learning and memory deficits in rodents. Image processing and pattern recognition can be used to analyse videos and recognize automatically the tracking in MWM. There are several commercial and free access software that allows analyzing the behavioral tasks although they also have limitations such as automation, cost, user intervention among other things. The aim of this paper was to develop a new image processing technique to automatically analyse the track of the rat in the MWM, which we called RatsTrack. The MWM test was performed with an animal model for Alzheimer, and the videos were recorded to measure the distance, time, and speed. The segmentation method based on the projection of the video frames was made for pool identification, eliminating the rat, while conserving the shape of the pool. Then, the Hough transformation was used to recognize the position and radius of the pool. Finally, the frame in which the rat is released into the pool was established automatically using mathematical morphology techniques and added as a plugin on free access ImageJ software. The new image processing technique, RatsTrack, successfully detected and located the pool and rat without user intervention, significantly decreasing operational time and providing results for distance, time, speed, and acceleration parameters of the MWM test. Alzheimer's rats compared with the control group presented significant data measured with the RatsTrack. RatsTrack is a plugin of ImageJ software and will be made freely available for public use.

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does.

Industrial parts often contain a large number of circular holes that are often used as assembly datums. Measuring circular holes with high accuracy is very challenging due to the low signal-to-noise ratio of the edges on the image. This problem is further complicated when chamfers and nonuniform reflections are present. In this paper, a high-accuracy method for measuring circular holes is proposed to realize on-line inspection under a multi-camera system. In the image processing stage, an ellipse detector is designed based on a high-confidence edge detection algorithm and state-of-the-art ellipse fitting technology to significantly eliminate the ellipse fitting bias caused by blurred edges. In the ellipse matching and validation stage, the ambiguous objects are reconstructed first using ambiguous ellipse pairs matched by the epipolar geometry. Then, the similarity between the reprojected ellipse and the detected ellipse is assessed by calculating the Euclidean distance to significantly eliminate mismatched candidates and select the best matching ones. Subsequently, the discrete 3-D points are calculated by spatial intersection after obtaining the homologous points on the corresponding ellipses. Furthermore, a robust spatial circle fitting method is developed to adapt to discrete 3-D points that are not strictly located on a plane. Finally, extensive experiments on both real-world datasets and simulation datasets demonstrate that the proposed algorithm is beneficial for improving the measurement accuracy compared with the state-of-the-art algorithms and demonstrate great potential for industrial applications. • A circular hole measurement method is proposed based on multi-camera systems. • An ellipse detector is designed to adapt to the edge that has a low signal-to-noise ratio. • An ellipse matching and validation algorithm is developed to match the correct ellipse. • A robust circle fitting algorithm is proposed for the discrete 3-D points. • Experiments demonstrate the high-accuracy measurement results and robustness. [ABSTRACT FROM AUTHOR]

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used. [ABSTRACT FROM AUTHOR]

The relevance of the tasks of detecting and recognizing objects in images and their sequences has only increased over the years. Over the past few decades, a huge number of approaches and methods for detecting both anomalies, that is, image areas whose characteristics differ from the predicted ones, and objects of interest, about the properties of which there is a priori information, up to the library of standards, have been proposed. In this work, an attempt is made to systematically analyze trends in the development of approaches and detection methods, reasons behind these developments, as well as metrics designed to assess the quality and reliability of object detection. Detection techniques based on mathematical models of images are considered. At the same time, special attention is paid to the approaches based on models of random fields and likelihood ratios. The development of convolutional neural networks intended for solving the recognition problems is analyzed, including a number of pre-trained architectures that provide high efficiency in solving this problem. Rather than using mathematical models, such architectures are trained using libraries of real images. Among the characteristics of the detection quality assessment, probabilities of errors of the first and second kind, precision and recall of detection, intersection by union, and interpolated average precision are considered. The paper also presents typical tests that are used to compare various neural network algorithms.

Nowadays, with the advances and use of technological possibilities and devices, the number of digital images is increasing gradually. Computer-aided classification of image types is widely applied in many applications such as medicine, security, and automation. The feature extraction and selection stages have great importance in terms of improving the classification performance as sub-stages of the pattern recognition process. Researchers apply different feature extraction methods for their works due to the requirements. In this study, a novel pattern recognition framework combining diverse and large-scale handcrafted feature extraction methods (shape-based and texture-based) and the selection stage on images is developed. Genetic algorithms are also used for feature selection. In the experimental studies, Flavia leaf recognition, Caltech101 object classification image datasets, and five supervised classification models (random forest, ECOC-SVM, k-nearest neighbor, AdaBoost, classification tree) with different parameters' values are used. The experimental results show that the proposed method achieves 98.39% and 82.77% accuracy rates on Flavia and Caltech101 datasets with the ECOC-SVM model, respectively. The proposed framework is also competitive with the existing state-of-the-art methods in the related literature. [ABSTRACT FROM AUTHOR]

License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge detection techniques, along with the bounding box method for identifying and revealing license plate characters while removing unwanted artifacts such as dust and fog. The mathematical model of foggy images is presented and the sum of gradients of the image, which represents the visibility of the image, is improved. Previous works on license plate recognition have utilized non-intelligent pattern matching techniques. The proposed technique can be applied in a variety of settings, including traffic monitoring, parking management, and law enforcement, among others. The applied algorithm, unlike SOTA-based methods, does not need a huge set of training data and is implemented only by applying standard templates. The main advantages of the proposed algorithm are the lack of a need for a training set, the high speed of the training process, the ability to respond to different standards, the high response speed, and higher accuracy compared to similar tasks.

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