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With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images. To address these challenges, we propose an end-to-end PMRR method based on a decoupled circle head detection algorithm (YOLOX-DC) and a Unet-like pure Transformer segmentation network (PM-SwinUnet). First, according to the characteristics of the pointer dial, the YOLOX-DC detection algorithm is designed based on the exceeding you only look once detector (YOLOX). The decoupled circle head of YOLOX-DC detects the pointer meter dial more accurately than the commonly used rectangular detection head. Second, the window multi-head attention of the PM-SwinUnet network enhances the feature extraction ability of pointer meter images and solves problems of missed scale detection and incomplete pointer segmentation. Additionally, the scale and pointer fitting module is introduced into the PM-SwinUnet to locate the accurate position of the scale and pointer. Finally, through the angle relationship between the pointer and the first two main scale lines, the pointer meter reading is accurately calculated by the improved angle method. Experimental results demonstrate the effectiveness and superiority of the proposed end-to-end method across three-pointer meter datasets. Furthermore, it provides a rapid and robust approach to the digital transformation of manufacturing systems. [ABSTRACT FROM AUTHOR]

An important paradigm in industrial engineering for fault detection and diagnosis purposes is signal processing. The various methods consider methods in the time, frequency, or time–frequency domain for signal processing as state and output signals from the considered process. The objective of this work is to perform a comparative analysis of the most used methods based on a signal processing paradigm and in the context of fault detection and process diagnosis. The electromechanical equipment that generates mechanical vibrations—as an effect of bearing faults—is considered and analyzed. The recorded data are explored with smaller and sliding frames, adapted to the processing criteria used. Seven methods are considered for evaluation: two in the time domain, two in the frequency domain and three in the time–frequency domain. The main problem is to extract and select the right features to use in the classification stage. The methods of the time domain are based on statistical moments and signal modeling. The methods in the frequency domain use either the discrete components of power spectra or the features of the frequency domain. In the time–frequency domain, the coefficients of the time–frequency transforms define digital images, which are further processed. For testing, the methods are evaluated with real recorded data from bearings with several types and sizes of faults, i.e., incipient, medium, advanced, and large. Finally, the considered methods are compared from the point of view of five criteria, namely, the recognition rate, window length, response time, computational resources, and complexity of the algorithms. A global quality criterion is built and used to assess the quality of the methods. The results of the computer-based experiments show acceptable performance for all methods for the test case of bearings but the potential to detect more complex faults and change detection in the behavior of the machines, in general. Time–frequency methods offer an optimum.Article highlights: A practical comparative overview of the main methods based on signal processing paradigm used in process diagnosis and detection problem. Comparing methods from different domains of representations: time, frequency, and time-frequency domain. An example of quality criterion in assessing the signal processing methods for process diagnosis and fault detection. [ABSTRACT FROM AUTHOR]

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Computer Vision and Biometrics benefit from the recent advances in Pattern Recognition and Artificial Intelligence, which tends to make model-based face recognition more efficient. Also, deep learning combined with data augmentation tends to enrich the training sets used for learning tasks. Nevertheless, face recognition still is challenging, especially because of imaging issues that occur in practice, such as changes in lighting, appearance, head posture and facial expression. In order to increase the reliability of face recognition, we propose a novel supervised appearance-based face recognition method which creates a low-dimensional orthogonal subspace that enforces the face class separability. The proposed approach uses data augmentation to mitigate the problem of training sample scarcity. Unlike most face recognition approaches, the proposed approach is capable of handling efficiently grayscale and color face images, as well as low and high-resolution face images. Moreover, proposed supervised method presents better class structure preservation than typical unsupervised approaches, and also provides better data preservation than typical supervised approaches as it obtains an orthogonal discriminating subspace that is not affected by the singularity problem that is common in such cases. Furthermore, a soft margins Support Vector Machine classifier is learnt in the low-dimensional subspace and tends to be robust to noise and outliers commonly found in practical face recognition. To validate the proposed method, an extensive set of face identification experiments was conducted on three challenging public face databases, comparing the proposed method with methods representative of the state-of-the-art. The proposed method tends to present higher recognition rates in all databases. In addition, the experiments suggest that data augmentation also plays an essential role in the appearance-based face recognition, and that the CIELAB color space (L*a*b) is generally more efficient than RGB for face recognition as it attenuates lighting variations. [ABSTRACT FROM AUTHOR]

Abstract Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. In order to develop a robust prediction model, this research work proposes three distinct architectural models to produce a facial expression prediction system that looks like this: The first model is on using a support vector machine to carry out a classification task. As a follow-up to the second model, an attempt was made to create a Convolution Neural Network (CNN) using the VGG-NET (Visual Geometry Group Network). Following analysis of the results, an attempt was made to enhance the outcome using the third model, which used convolutional sequential layers linked to seven distinct expressions, and an inference was drawn based on loss and accuracy metric behavior. We will use a dataset of human picture facial images in this research, which has more than 35500 facial photographs and represents seven different types of facial expressions. We will analyze our data and make every effort to remove as much noise as we can before feeding that information to our model. We use the confusion matrix to assess the model’s performance after it has been implemented effectively. To demonstrate the effectiveness of our model architecture, we will generate bar graphs and scatter plots for each model to display model loss and accuracy. The output of this model is visualized with actual class and predictive class and the result has a graphical representation for each and every output facial Images which makes our recognition system user-friendly.

The human brain is a highly efficient structure that can easily perform various complex tasks, such as shape recognition, presentation, and classification, while consuming minimal energy and occupying only a small volume. This study introduces a bio‐inspired electrolyte‐gated neuromorphic transistor that mimics the functionality of the human brain. A dual‐electrolyte structure combining lithium phosphorus oxynitride and lithium silicate achieves the best performance, with a mobility of 3.1 cm2 V−1 s−1, a paired‐pulse facilitation index of 162.6%, and nonlinearity coefficients of 0.02 and 0.03 (for potentiation and depression, respectively). Further, risk pre‐detection and image recognition are successfully demonstrated using the developed dual‐electrolyte synaptic transistors. A test conducted on the Modified National Institute of Standards and Technology database indicates an accuracy of 91.0%. Thus, the device has the potential to advance artificial vision systems. [ABSTRACT FROM AUTHOR]

Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. In order to develop a robust prediction model, this research work proposes three distinct architectural models to produce a facial expression prediction system that looks like this: The first model is on using a support vector machine to carry out a classification task. As a follow-up to the second model, an attempt was made to create a Convolution Neural Network (CNN) using the VGG-NET (Visual Geometry Group Network). Following analysis of the results, an attempt was made to enhance the outcome using the third model, which used convolutional sequential layers linked to seven distinct expressions, and an inference was drawn based on loss and accuracy metric behavior. We will use a dataset of human picture facial images in this research, which has more than 35500 facial photographs and represents seven different types of facial expressions. We will analyze our data and make every effort to remove as much noise as we can before feeding that information to our model. We use the confusion matrix to assess the model's performance after it has been implemented effectively. To demonstrate the effectiveness of our model architecture, we will generate bar graphs and scatter plots for each model to display model loss and accuracy. The output of this model is visualized with actual class and predictive class and the result has a graphical representation for each and every output facial Images which makes our recognition system user-friendly. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification, drawing insights from literature across renowned repositories. This paper critically summarizes relevant literature based on AI algorithms, extracted features, and results achieved. Additionally, it analyzes extensively used datasets in automated plant classification research. It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition. Moreover, this rigorous review study discusses opportunities and challenges in employing these AI-based approaches. Furthermore, in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions. This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants. [ABSTRACT FROM AUTHOR]

Infrared thermography is an emerging technique in biomedical research, potentially providing diagnostic insights into psychological stress, physical strain, muscle fatigue, inflammation, tissue damage, and diseases with thermogenic effects. However, manual analysis strategies are frequently applied causing incomparable, non-reproducible results and hampering standardization. Moreover, widely applied manual analysis cannot recognize blood vessel-related thermal radiation patterns during physical exercise. Therefore, an enhanced processing pipeline, "ThermoNet", has been developed to automatically process thermograms captured during running. For acquisition, an automatic temperature calibration technique has been introduced to obtain reliable pixel-temperature mapping. The thermograms are semantically segmented in the processing pipeline to extract the anatomical regions of interest (ROIs) by a state-of-the-art deep neural network rather than considering both legs as a single area. A second neural network further examines the ROIs to identify different venous and arterial (perforator) patterns. Within the segments, advanced statistical features are computed to provide time series data. Separate analysis of venous and perforator vessel patterns is carried out on individual connected components, resulting in the extraction of 276 features for each thermogram. The enhanced ROI extraction achieved a high accuracy for the left and right calf on the manually annotated test set. Each step of the ThermoNet pipeline represents a significant improvement over previous analysis methods. Finally, ThermoNet is a transferable pipeline for automatic, reproducible, and objective analysis of ROIs in thermal image sequences of moving test individuals. [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]

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent's pieces and moving the captured piece outside the board workspace. [ABSTRACT FROM AUTHOR]

Creutzfeldt–Jakob disease (CJD) is a rare, degenerative, and fatal brain disorder caused by abnormal proteins called prions. This research introduces a novel approach combining AI and graph theory to analyze histopathological microscopic images of brain tissues affected by CJD. The detection and quantification of spongiosis, characterized by the presence of vacuoles in the brain tissue, plays a crucial role in aiding the accurate diagnosis of CJD. The proposed methodology employs image processing techniques to identify these pathological features in high-resolution medical images. By developing an automatic pipeline for the detection of spongiosis, we aim to overcome some limitations of manual feature extraction. The results demonstrate that our method correctly identifies and characterize spongiosis and allows the extraction of features that will help to better understand the spongiosis patterns in different CJD patients. [ABSTRACT FROM AUTHOR]

Abstract Lip‐reading deciphers speech by observing lip movements without relying on audio data. The rapid advancements in deep learning have significantly improved lip‐reading for both English and Chinese; however, research on dialects such as Cantonese remains scarce. Consequently, most Chinese lip‐reading datasets focus on Mandarin, with only a few addressing Cantonese. To bridge this gap, a sentence‐level Cantonese lip‐reading dataset, designated as Cantonese lip‐reading sentences are introduced, comprising over 500 unique speakers and more than 30,000 samples. To ensure alignment with real‐world scenarios, no restrictions are imposed on factors such as gender, age, posture, lighting conditions, or speech rate. A comprehensive description of the pipeline employed is provided for collecting and constructing the dataset and introduce an innovative visual frontend, 3D‐visual attention net. This frontend combines the advantages of convolution and self‐attention mechanisms to extract fine‐grained lip region features. These features are subsequently input into the conformer backend for temporal sequence modelling, achieving comparable performance on Chinese Mandarin lip reading dataset, lip reading sentences 2, lip reading sentences 3, and Cantonese lip‐reading sentences datasets. Benchmark tests on Cantonese lip‐reading sentences demonstrate the challenges it poses, providing a novel research foundation for dialect lip‐reading and fostering the advancement of Cantonese lip‐reading tasks.

Convolutional neural networks (CNNs) are used to solve pattern classification problems. As this algorithm is based on establishing a relationship between an image-shaped input and its related output through the CNN structure, the training stage is a significant process in their working basis. This study develops a new-fangled and explainable algorithm to train CNNs. The input filters in the convolution layers are parameterized to keep the filter structure, implementing traditional and explainable image processing filters within the network topology. A back-propagation scheme updates the parameters in the filters and the fully connected section of the CNN. Several traditional image filters (Sobel, averaging, Gaussian, and directional, among others) are used in CNN with a learning strategy that keeps their kernel structures. The method implies that the training of these networks is applied to a single parameter instead of all coefficients in the filters, reducing the uncertainty about how each filter performs the image analysis in CNN. This approach was compared with traditional CNNs considering the analysis of the computational cost (measured in terms of time and floops required for training) and their accuracy results. Three image databases were used to evaluate the proposed algorithm. Using a cross-validation methodology, the new training algorithm based on the filter parameterization achieved higher accuracy (93.7% vs. 91.3% in average). The new algorithm got similar results regarding the computational cost compared to traditional methods. This characteristic makes the proposed training methodology an appropriate option to classify images with more explainable processing at the convolution layers. [ABSTRACT FROM AUTHOR]

Lip‐reading deciphers speech by observing lip movements without relying on audio data. The rapid advancements in deep learning have significantly improved lip‐reading for both English and Chinese; however, research on dialects such as Cantonese remains scarce. Consequently, most Chinese lip‐reading datasets focus on Mandarin, with only a few addressing Cantonese. To bridge this gap, a sentence‐level Cantonese lip‐reading dataset, designated as Cantonese lip‐reading sentences are introduced, comprising over 500 unique speakers and more than 30,000 samples. To ensure alignment with real‐world scenarios, no restrictions are imposed on factors such as gender, age, posture, lighting conditions, or speech rate. A comprehensive description of the pipeline employed is provided for collecting and constructing the dataset and introduce an innovative visual frontend, 3D‐visual attention net. This frontend combines the advantages of convolution and self‐attention mechanisms to extract fine‐grained lip region features. These features are subsequently input into the conformer backend for temporal sequence modelling, achieving comparable performance on Chinese Mandarin lip reading dataset, lip reading sentences 2, lip reading sentences 3, and Cantonese lip‐reading sentences datasets. Benchmark tests on Cantonese lip‐reading sentences demonstrate the challenges it poses, providing a novel research foundation for dialect lip‐reading and fostering the advancement of Cantonese lip‐reading tasks. [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]

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]

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]

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]

Object detection is a fundamental task in computer vision with diverse applications across various domains. Template matching, a classical technique, remains a significant method for detecting objects in images. This paper presents a novel approach to object detection using template matching, leveraging the correlation between a reference template and regions within an image to identify instances of the target object. The proposed method involves the extraction of feature templates representing the object of interest and employing similarity metrics to locate these templates within the image. The process encompasses various steps, including template creation, correlation computation, and localization, enabling accurate object detection while considering scale, rotation, and partial occlusion. Through experiments conducted on benchmark datasets, the effectiveness and robustness of the proposed template matching approach are demonstrated, showcasing its potential for real-world object detection applications with promising results in terms of accuracy and computational efficiency. [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.

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]

Today, face recognition is the most prevalent and effective mechanism among various biometric technologies as it is non-invasive method. It helps in identifying or verifying the identity of a person by utilizing its face. But face recognition (FR) can be prone to high error rate. Therefore, efficient feature extraction methods are required for extracting robust facial features to develop efficient FR system. A FR system comprises of mainly three phases, that is, face detection and orientation, extracting facial features and classification of features. The most vital part of an efficient recognition system is extraction of robust features. Hence, extracting facial features is active research area of image processing. Although algorithms have been developed for extracting features, efficient and robust feature extraction still offers great challenge to researchers. Hence, a thorough analysis of feature extraction techniques presented in this work will enable the researchers to select the best suited technique for developing efficient system for recognition of face images. The performance of different feature extraction methods varies under variations in illumination, occlusion and pose etc. It is observed that deep-learning based feature extraction methods outperform wavelet-based methods. After rigorous analysis of various state-of-art techniques it is found that highest accuracy rate of 99.5% is achieved on AR database using wavelet based feature extraction whereas 99.78% is attained using convolutional neural network accuracy and various results achieved till now are stated. [ABSTRACT FROM AUTHOR]

Ultrasound images and ultrasound imaging method is an effective method in examining the challenges, problems and diseases related to the breast in women. The contrast of these images is generally very weak, however, the tumor tissue and calcium grains are evident in it. Methods based on image processing are widely used in breast tumor diagnosis and classification. In this article, a method based on pattern recognition is presented in order to detect the type of tumor. GLCM-based features are extracted from the target area, and Gabor and texture features. Then it is reduced with the help of dimension reduction methods based on principal component analysis. Finally, with the help of the improved classification of Ada KKN with ELM, they are grouped into three categories. Evaluation criteria such as Accuracy (98.81%), sensitivity (91.51%) and specificity (94.54%) compared to other similar methods show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

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]

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.

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent’s pieces and moving the captured piece outside the board workspace.

Abstract 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.

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]

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]

Smart manufacturing needs cognitive computing methods to make the relevant systems more intelligent and autonomous. In this respect, bio-inspired cognitive computing methods (i.e., biologicalization) can play a vital role. This article is written from this perspective. In particular, this article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it cannot get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, but not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like). This characteristic of DBC can help to solve cognitive problems (e.g., pattern recognition). DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, we demonstrate that DBC-1 can solve various cognitive problems, e.g., "similarity indexing between seemingly different but inherently identical objects" and "recognizing regions of an image separated by a complex boundary." In addition, using an arbitrary numerical example, we demonstrate that DBC-2 can solve the following cognitive problem: "pattern recognition when the relevant information is insufficient." The remarkable thing is that smart manufacturing-based systems (e.g., digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (e.g., machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and enrich their biologicalization. [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 provides a short survey of the main direction of activity of the Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences (FRC CSC RAS) in the areas concerning the topics of the journal Pattern Recognition and Image Analysis. The FRC CSC RAS was founded in December 2014 through the reorganization of the Institute of Informatics Problems of the Russian Academy of Sciences (IIP RAS) (by including the Institute for System Analysis of the Russian Academy of Sciences (ISA RAS) and the Dorodnycin Computing Center of the Russian Academy of Sciences (DCC RAS)). Section 1 reviews the activity of these institutes before their merger. Section 2 studies the work performed after 2015. [ABSTRACT FROM AUTHOR]

Preface to the Special Issue of the journal PRIA "Leading Scientific Schools in Image Analysis, Pattern Recognition, and Related Problems of Computer Science in the USSR, the Republic of Belarus and the Russian Federation." The main scientific schools, their scientific leaders, and basic organizations are presented. The most important results obtained by these scientific schools are discussed. A historical and analytical review of the activities of scientific schools is given. The topics and main directions of fundamental and applied research in image analysis and pattern recognition are considered. Tools and methods for coordinating and supporting the development of domestic image analysis and pattern recognition (international scientific cooperation, conferences, publications) are listed, analyzed, and discussed. Current problems, the state of the art, and breakthrough directions in the development of mathematical theories of image analysis and pattern recognition are identified. [ABSTRACT FROM AUTHOR]