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Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.

Spin-Transfer-Torque (STT) MRAM is configured with high access speed, readily hybrid integration and guaranteed endurance/retention, which becomes one of the most promising candidates for next generation memory. However, the major challenge of STT-MRAM is its high switching energy consumption, accompanied with unreliable writing operation. This work exploits two emerging design paradigms entitled approximation and speculation MRAM. A novel design scheme with timing speculation and approximate storage is proposed to improve energy efficiency in MRAM write operation by reducing write time. Simulation results show that latency and energy loss in entire write operation can be significantly reduced. The proposed technique obtains 84%/57.4% in the symmetric and unidirectional writing operations. Meanwhile, timing speculative method improves the failure/reliability in writing by re-write strategy. Related MRAM macro specification is applied to image processing, which obtains 10% (58.8%-67.2%) of the improvement of precision in image and higher image quality is obtained under the same accuracy rate.

Field imagery is an effective way to capture the state of the entire field; yet, current field inspection approaches, when accounting for image resolution and processing speed, using existent imaging systems, do not always enable real-time field inspection. This project involves the innovation of novel technologies by using an FPGA-based image processing (FIP) device that eliminates the technical limitations of the current agricultural imaging services available in the market and will lead to the development of a market-ready service solution. The FIP prototype developed in this study was tested in both a laboratory and outdoor environment by using a digital single-lens reflex (DSLR) camera and web camera, respectively, as the reference system. The FIP system had a high accuracy with a Lin’s concordance correlation coefficient of 0.99 and 0.91 for the DLSR and web camera reference system, respectively. The proposed technology has the potential to provide on-the-spot decisions, which in turn, will improve the compatibility and sustainability of different land-based systems.

Civil steel structures and infrastructures, such as truss railway bridges, are often subject to potential damage, mainly due to fatigue phenomena and corrosion. Therefore, damage detection algorithms should be designed and appropriately implemented to increase their structural health. Today, the vast amount of information provided by data processing techniques and measurements coming from a monitoring system constitutes a possible tool for damage identification in terms of both detection and description. For this reason, the research activity aims to develop a methodology for a preliminary description of the damage in steel railway bridges induced by fatigue phenomena. The proposed approach is developed through an integration of global and local procedures. At the global scale, vibration-based procedures will be applied to improve a forecast numerical model and, subsequently, to identify the zones most involved in fatigue problems. At the local scale, careful and refined local identification will be pursued via image processing techniques whose evidence will be analyzed and described through nonlinear numerical models. A case study of a historical railway bridge in Spain will illustrate the methodology’s performance, potentiality, and critical issue.

One of the primary issues with inside the agricultural region is crop sicknesses and automated detection is crucial for crop monitoring. Plant leaves generally display the maximum ailment symptoms, however professional laboratory leaf analysis is high priced and time-consuming. According to the Food and Agriculture Organization (FAO), agricultural pests lessen crop yields international via way of means of 20 to 40% according to year. Smart farming is good solution for farmers is to apply artificial intelligence techniques along with modern statistics and communication technologies to get rid of those dangerous insect infestations. Farmers can substantially lessen financial losses via way of means of treating plants directly and detecting sicknesses and pests in apple, mango and graphs leaves appropriately and timely. Image type accuracy has progressed notably because of latest advances in deep studying-primarily based totally convolutional neural networks (CNN). This article evolved strategies primarily based totally on deep studying to hit upon sicknesses and pests in apples, mango, and grape leaves. These strategies have been influenced via way of means of the achievement of CNNs in photograph type.

In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses.&nbsp

Plastic straws are well-known tools to assist human beings in drinking fluid, but most of them have micro-defects including black spot defects, head problems, pressure tube defects, and sealing wrinkles. The manual detection of these defects has drawbacks such as low efficiency, a high false detection rate, and excessive labor. This paper proposed machine vision-based detection with self-adaption and high-accuracy characteristics. A serial synthesis of algorithms including homomorphic filtering, Nobuyuki Otsu, and morphological opening operations is proposed to obtain plastic straws with binary images with good performance, and it was further found that the convolutional neural network can be designed to realize the real-time recognition of black spot defects, where the corner detection algorithm demonstrates the linear fitting of the edge point of the straw with the effective detection of sealing wrinkle defects. We also demonstrated that the multi-threshold classification algorithm is used to detect defects effectively for head problems and pressure tube defects. The detection system based on machine vision successfully overcomes shortcomings of manual inspection, which has high inspection efficiency and adaptively detects multiple defects with 96.85% accuracy. This research can effectively help straw companies achieve high-quality automated production and promotes the application of machine vision in plastic straw defects with the aid of machine learning.

The paper is devoted to machine learning methods that focus on texture-type image enhancements, namely the improvement of objects in images. The aim of the study is to develop algorithms for improving images and to determine the accuracy of the considered models for improving a given type of images. Although currently used digital imaging systems usually provide high-quality images, external factors or even system limitations can cause images in many areas of science to be of low quality and resolution. Therefore, threshold values for image processing in a certain field of science are considered. The first step in image processing is image enhancement. The issues of signal image processing remain in the focus of attention of various specialists. Currently, along with the development of information technology, the automatic improvement of images used in any field of science is one of the urgent problems. Images were analyzed as objects: state license plates of cars, faces, sections of the field on satellite images. In this work, we propose to use the models of Super-Resolution Generative Adversarial Network (SRGAN), Extended Super-Resolution Generative Adversarial Networks (ERSGAN). For this, an experiment was conducted, the purpose of which was to retrain the trained ESRGAN model with three different architectures of the convolutional neural network, i.e. VGG19, MobileNet2V, ResNet152V2 to add perceptual loss (by pixels), also add more sharpness to the prediction of the test image, and compare the performance of each retrained model. As a result of the study, the use of convolutional neural networks to improve the image showed high accuracy, that is, on average ESRGAN+MobileNETV2 – 91 %, ESRGAN+VGG19 – 86 %, ESRGAN+ResNet152V2 – 96 %., Робота присвячена методам машинного навчання, орієнтованим на покращення зображень текстурного типу, а саме на поліпшення об'єктів на зображеннях. Метою дослідження є розробка алгоритмів покращення зображень та визначення точності розглянутих моделей для покращення даного типу зображень. Незважаючи на те, що використовувані в даний час системи цифрової обробки зображень зазвичай забезпечують високу якість зображень, зовнішні фактори або навіть системні обмеження можуть привести до того, що зображення в багатьох областях науки матимуть низьку якість та роздільну здатність. Тому розглядаються порогові значення для обробки зображень у певній галузі науки. Першим етапом обробки зображень є покращення зображення. Питання обробки зображень сигналів залишаються у центрі уваги різних фахівців. В даний час, поряд з розвитком інформаційних технологій, одним із актуальних завдань є автоматичне покращення зображень, що використовуються в будь-якій галузі науки. Зображення аналізувалися як об'єкти: державні номерні знаки автомобілів, особи, ділянки поля на супутникових знімках. У даній роботі ми пропонуємо використовувати моделі генеративно-змагальної мережі надроздільної здатності (SRGAN), розширених генеративно-змагальних мереж надроздільної здатності (ERSGAN). Для цього був проведений експеримент, метою якого було перенавчання навченої моделі ESRGAN з трьома різними архітектурами згорткової нейронної мережі, тобто VGG19, MobileNet2V, ResNet152V2 для додавання втрат сприйняття (за пікселями), а також додавання різкості до прогнозування тестового зображення та порівняння продуктивності кожної перенавченої моделі. В результаті дослідження використання згорткових нейронних мереж для покращення зображення показало високу точність, тобто в середньому ESRGAN+MobileNetV2 – 91 %, ESRGAN+VGG19 – 86 %, ESRGAN+ResNet152V2 – 96 %

Violating the observation conditions for the investigated objects leads to the formation of diverse typical forms of objects throughout the frame in the series. As a consequence, determining the exact position of the object on the frame becomes difficult. To this end, a method was devised to determine the position of an object using the typical form of its image on a series of frames. This method is based on the formation of a typical form of a digital image of an object based on data from all frames of the series. This makes it possible to take into account the peculiarities of the very formation of the digital image of an object on each frame of the original series. Based on this, a more accurate assessment of the initial approximation of the parameters of all Gaussians of the object's image is performed. Adapting the method specifically for the typical form allows for a more accurate assessment of the positional parameters (coordinates) of the object in comparison with the analytically set profile. The estimation of the position of an object was obtained using the method of least squares. After that, minimization was performed using the Levenberg-Marquardt algorithm. Also, the use of the method makes it possible to improve identification with reference objects and reduce the number of false detections. The study showed a reduction in the standard deviation of frame identification errors by 7–10 times when using a typical digital image shape. The method devised for determining the position of an object using the typical form of its image was tested in practice within the framework of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software to automatically detect new objects and track known ones. Owing to the use of Lemur software and the proposed computational method implemented in it, more than 700,000 measurements of various objects under study were successfully processed and identified

Granular soils are used in different areas of civil engineering due to their easy accessibility and low cost as a material. The sieve analysis method, which has been conducted in rock physics measurements for many years, has been practicing determining the particle size distributions for those materials. In this study, a method based on image analysis technique has been developed as an alternative to traditional sieve analysis method for determining the particle size distribution in granular soils. This technique based on image processing to determine particle distributions via the experimental setup that consist of a camera, tripod, light box and mechanical shaking apparatus. In order to assess the reliability of this technique, each sample was subjected to traditional sieve analysis and the results of both analysis methods were compared. In conclusion, it was observed that the results obtained with the image processing technique had a minimum 95.22% closeness with the sieve analysis experiment data.

Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

本稿では、画像処理の古写真への応用について議論する。具体的な応用として、立札に記載された文字の可読性の向上と既存の深層学習に基づいた方法で着色された画像の品質改善を試みる。また、画像処理にPythonを用いることで、オープンソースのソフトウェアのみで処理が完結できることを目指す。, In this article, we discuss the application of image processing to old photographs. As concrete applications, we attempt to enhance the legibility of the characters on a notice board and to improve the quality of the image colored using the existing deep learning-based method. In addition, by using Python for image processing, we aim to complete the process using only open-source software.

The Ministry of Health of the Republic of Indonesia is running a program to eliminate Tuberculosis (TB) by 2030. At the Primary Health Care level, AFB (acid-fast bacteria) examination confirms the TB diagnosis. In this process, the patient's sputum is prepared in the form of preparation and observed by the laboratory analyst through the lens of a microscope. The reporting process to establish this diagnosis requires calculating the number of TB bacteria in 100 fields of view per preparation. This manual microscopic observation process is tedious, and the reading results are subjective. This study offers an integrated design for automatic microscopic imaging with a computer-integrated TB bacteria detection system. The process of taking pictures is automatically obtained with the help of a driving motor added to the microscope. With the addition of this motor, the process of taking microscopic images for 100 fields of view takes ±450 seconds. The proposed system integration process can reduce laboratory analysts' work fatigue in conducting microscopic observations manually. The TB bacteria detection system utilizes the working principle of image processing techniques by combining color-deconvolution, segmentation, and contour-detection methods. The comparative value of the TB object detection system with experts resulted in a sensitivity value of 77% and a specificity value of 68%. However, the low detection rate is because the image obtained is still blurry. Thus, further investigation is needed to determine the driving motor's movement rate and the right timing for taking microscopic images so that the resulting image is not blurry. The final result that is the focus of this paper is the successful integration of the system carried out between the motor drive system on the preparation stand and the TB bacteria detection system to become a unified system.

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%-50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.

Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area. Most studies have focused on recognizing diseases from images of whole leaves. This approach limits the resulting models’ ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf. Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy. In those studies, however, the lesions were laboriously cropped by hand. This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem. These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network (CNN) models, respectively. We report that GoogLeNet’s disease recognition accuracy improved by more than 15% when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves. A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper. The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union (mIoU) score of 0.8448 and 0.6257 for the leaf and lesion pixel classes, respectively. In terms of mean boundary F1 score, the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes, respectively. Lastly, a fully automatic algorithm for leaf disease recognition from individual lesions is proposed. The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition. The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting. Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce. This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models, i.e. Multiple Linear Regression (MLR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). One-way analysis of variance was applied to reduce RGB, HSV, and L*a*b* features number of hydroponic lettuce images. Image binarization, image mask, and image filling methods were employed to segment hydroponic lettuce from an image for models testing. Results showed that G, H, and a* were selected from RGB, HSV, and L*a*b* for training models. It took about 20.25 s to detect an image with 3 024 × 4 032 pixels by KNN, which was much longer than MLR (0.61 s) and SVM (1.98 s). MLR got detection accuracies of 89.48% and 99.29% for yellow and rotten leaves, respectively, while SVM reached 98.33% and 97.91%, respectively. SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic. Thus, it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

Automatic image detection is one of the most im- portant areas in computing due to its potential application in numerous real-world scenarios. One important tool to deal with that is called overlap indices. They were introduced as a procedure to provide the maximum lack of knowledge when comparing two fuzzy objects. They have been successfully applied in the following fields: image processing, fuzzy rule-based systems, decision making and computational brain interfaces. This notion of overlap indices is also necessary for applications in which type-2 fuzzy sets are required. In this paper we introduce the notion of type-(2, k) overlap index (k 0, 1, 2) in the setting of type-2 fuzzy sets. We describe both the reasons that have led to this notion and the relationships that naturally arise among the algebraic underlying structures. Finally, we illustrate how type- (2, k) overlap indices can be employed in the setting of fuzzy rule-based systems when the involved objects are type-2 fuzzy sets. This manuscript has been partially supported by Junta de Andalucía by Projects A-FQM-170-UGR20 (Program FEDER Andalucía 2014-2020) and FQM-365 (Andalusian CICYE), and also by Projects PID2020-119478GBI00 and PID2019-108392GB-I00 (AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación), by CNPq (301618/2019-4), FAPERGS (19/2551- 0001660) and grant VEGA 1/0267/21.

The appearance of "blurred" digital images is a consequence of the violation of the immobility of the camera during the shooting of the objects under study. To this end, a procedure was devised for matched filtering of the blurred digital image of the object using its typical image form in a series of frames. This procedure is based on the automated formation of a typical form of a digital image, as well as on the choice of special parameters for the transfer function of the matched filter. Adapting the procedure specifically to the typical form makes it possible to perform a more accurate assessment of the required parameters of the blurred digital image compared to the analytically set profile. The formation of a typical form makes it possible to take into account the features of the very formation of the blurred image on each frame of the original series. Based on this, a more accurate assessment of the initial approximation of the parameters of all Gaussians of the object image is performed. In practice, matched filtering makes it possible to highlight blurred images of objects against the background of substrate noise. Also, using the matched filtering procedure makes it possible to improve the segmentation of images of reference objects and reduce the number of false detections. The devised procedure for the matched filtering of a blurred digital image using its typical form has been tested in practice as part of the research in the framework of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software for the automated detection of new and tracking of known objects. Owing to the use of Lemur software and the proposed computational procedure introduced into it, more than 700,000 measurements of various objects under study were successfully processed and identified

The thermal blooming effect, platform jitter, and other effects of laser beam propagation cause serious deviation, which will have a negative impact on laser tracking systems. It is important to ensure that the laser beam does not deviate. Based on a fast steering mirror, a CMOS camera, and a Flex RIO system, a laser beam jitter control system is designed and implemented. The error is detected by camera and compensated for by the fast steering mirror (FSM), which is controlled by the Flex RIO device. By using LabVIEW and MATLAB, a closed loop model is realized. The results show that the system response is highly stabilized and has a short rise time, providing a reference for engineering applications.

An important advantage of optical signal processing is the possibility of performing various operations in parallel. Among the techniques proposed for parallel optical signal processing, frequency division multiplexing (FDM) is a well-known approach, in which different operations are performed in multiple sub-channels associated with different frequency bands. Here, we demonstrate the possibility of performing parallel optical signal processing by frequency division multiplexing of the edge modes of a topological photonic structure. This leads to the realization of a multiple-input-multiple-output (MIMO) topological optical signal processor that is capable of performing several analog functionalities simultaneously, enhancing the speed of computation significantly. Our theoretical and experimental findings open up the venue towards the realization of a new generation of multi-functional topological optical signal processing systems that are ultra-fast, consume little power, and enjoy strong protection against disorder.

In ship detection by means of Polarimetric SAR imagery, a very promising feature is the characterization of the pixels of the ship based on the elementary scattering mechanisms that can be extracted using different decomposition algorithms. Elementary scattering mechanisms provide information regarding the physical, electrical and geometrical properties of the scatterers in each Polarimetric SAR pixel. In this work, the newly established algorithm of the Double Scatterer Model is applied to interpret each pixel of the Polarimetric SAR image with the contributions of two elementary scattering mechanisms, namely, primary and secondary. The main idea is to construct a binary image while preserving the rich information content in order to proceed in simple and fast image processing for target detection. The present algorithm is applied to datasets with different inherent characteristics acquired by Radarsat-2 and ALOS-PALSAR. The results presented by this new perspective on ship monitoring are remarkable.

In laser powder bed fusion (LPBF) additive manufacturing, the melt pool (MP) characteristics are key indicators for process and part defects. For example, the laser scan location on the build plate...

Computer vision is nowadays is one of the promising & evolving areas in information technology. Image processing is increasingly used in several applications such as automotive, medical or aerospace. Every computer vision application involves capturing several images with the help of camera. Image acquisition, image transmission & image security are the three important aspects of image processing system. This paper includes development of complete image processing system including improved median filtering with better PSNR & operating frequency, Image compression module with better PSNR & compression ratio, Image Encryption module using Advanced Encryption Standard.

New method for Detection wheat stem rust disease using Image processing methods, Converting RGB image To Hue Saturation Value then setting HSV parameters for background deleting. Texture analyses used for feature extractions. Feature selections where used to accelerate accuracy of detection. Backpropagation neural network used for classifications.

Plants make up more than 80 percent of the human diet. As a result, they are critical for food security and ensuring that we all have access to enough, cheap, clean, and nutritional food to lead healthy and active living. This research focuses on plant diseases as they create a major threat to food security. Expert people are hired in traditional farming to physically evaluate line by line for host disease plants, which would be labor-intensive, time taking and potentially error-prone activity because it is done by people manually. This work focus on, supervised machine learning algorithms like RF, SVM, DT, KNN, NB, and KNN with image processing methods and also analysis algorithm results with each other and finds the best algorithm for plant disease classification. RF algorithm achieved 89 percent accuracy compared with other algorithms.

As the usage of vehicles is increasing day by day, it became difficult to identify a proper parking space for every vehicle. Smart Parking System deals with development of mobile application, that is useful for user to access detailed information about parking space and managing it efficiently in parking lot. Smart Parking System uses Image Processing technique to identify registration plate of a vehicle and also provides autonomous door opening and closing operation whenever it detects a vehicle at entrance of a parking lot. The mobile application provides information about available parking spaces, security and safety features of parking lot like fire and gas leakages alert. Raspberry Pi is the control unit which controls and processes the whole operation of the system. Liquid crystal display (LCD) display is located at entry point of the parking lot to display current parking space availability. Infrared (IR) proximity sensors are employed to identify the existence of vehicle at the entry gate of parking lot. From the captured image, characters from vehicle registration plate can be identified, and then Raspberry Pi sends a signal to servo motor to open the gate for a specified interval of time. The user will then park the vehicle in the available parking slot. When the user wants to leave the space and moved the vehicle, the date and time information will be captured which will be further used for processing the bill.

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose the Efficient Attention Pyramid Transformer (EAPT) in this paper. More specifically, we first propose Deformable Attention, which learns an offset for each position in patches. Therefore, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information between all patches to get more complete global attention information. Finally, we also proposed a position encoding specifically for vision transformers, which can be used for patches of any dimensions and any lengths. Extensive experiments on the vision tasks of image classification, object detection, semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure.

Recently, extraction of text or characters from images has become a wide field of research in the field of image analysis, neural networks, artificial intelligence and machine vision. The aim of character recognition is to extract and recognize the text from images and can be thus used to analyze contents, documents, or retrieve certain information etc. Image analysis has various processes and algorithms available for text extraction like edge detection, point detection, corner detection etc. for extracting the text from images. Many steps are involved in text recognition including pre-processing, segmentation, feature extraction, and classification. This paper addresses FAST algorithm for corner detection applied on the images for text recognition. The FAST algorithm detects and determines the presence of a corner by testing a small area around the potential center of the corner with the help of intensity value. This approach of text recognition can be efficiently used even with images with noise content or blurs.

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequential use of two components: homeomorphic data space transformation and synthetic data generation. Special attention is given to the privacy of image data. Specifics of image representation require special approaches towards encryption and synthetic image generation. We suggest use of (convolutional, variational) autoencoders and pre-trained feature extractors to enable applying privacy protection algorithms on top of the latent feature vectors captured from the images, and we updated the hybrid algorithms composed of homeomorphic transformation-as-encryption plus synthetic image generation accordingly. We show that an encrypted image can be reconstructed (by the pre-trained Decoder component of the convolutional variational autoencoder) into a secured representation from the extracted (by either the Encoder or a feature extractor) and encrypted (homeomorphic transformation of the latent space) feature vector. peerReviewed

The theme of this study is to provide a detailed description of its recent improvements in image segmentation and lesion classification in disease prognosis. Previous studies have shown that gray-white matter hyperintensities (GWMH) is one of the hallmarks of Nipah encephalitis, which sometimes occurs during the incubation period. Predicting this type of inflammation is a challenging task because it involves some unknown medical risk factors. A typical Magnetic Resonance Imaging (MRIs) is the best non-invasive system to analyze the anatomical structure of the brain. In-depth analysis of the defined pathological structure from isolated MR imaging leads to a reduction in the processing time of the prognostic model. Modern learning techniques such as Machine Learning, Computer Vision, and Deep Learning are the most promising techniques for determining the optimal outcome, computer can able to learn and extract useful information from historical data using various algorithms. Disease prognosis based on deep learning is sophisticated, so it can handle a variety of difficult tasks including image processing, classification, and feature extraction, noise and object detection. Diffusion-weighted imaging (DWI) in MRIs is a clinical prototype that can be used to diagnose brain abnormalities and to evaluate the microscopic architectural and molecular function of human organs or tissues. In this study, we summarize the results of diagnosing Nipah encephalitis using some publicly available brain encephalitis and encephalopathy databases.

This paper elucidates diverse algorithms that are used in drones for face identification. Face identification is the thriving technology that utilizes image processing for the identification of human faces. The main reasons for the booming interest in face recognition include the rise in population which demands high security and surveillance systems, requisite for identity verification in the digital world, for the combat in rural areas, alleviation during disasters, and so on. The goal of this paper is to explore, compare and contrast numerous face identification algorithms such as Linear Discriminant Analysis (LDA), Local Binary Pattern Histogram (LBPH), Principal Component Analysis (PCA), Elastic Bunch Graph Matching (EBGM) and neural networks. This study would assist the technologists in the field of face recognition to frame a hybrid algorithm based on the needs of the real time applications.

Traditional fire detection depends on smoke sensors. This strategy, however, is unsuited for big and open buildings, as well as outdoor regions. As a result, based on computer vision systems, this research proposes an effective method for recognizing flames in open areas. To minimize data size without losing important information, integer Haar lifting wavelet transform is used to frame and analyze the input video. Then, three color spaces (binary, hue, saturation, value (HSV), and YCbC) are used in simultaneous color detection. In binary space, Otsu’s approach is utilized to determine automated intensity pixels. Additionally, using frame differences to reduce false alarms. According to the experimental results, the approach achieves 99% accuracy for offline videos and surpasses 93% accuracy for real-time videos while maintaining a lower level of complexity.

Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.

The Fast Random Forest (FRF) image processing algorithm, being a machine learning supervised algorithms, allows to create a classifier model (training model) able to detect the same classes (features) in other images. In order to prove the efficiency of the FRF training model it is executed the proposedtest.&nbsp

This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling.

In this study, the license plate reading system, which is needed in security applications, automaticdoor systems, or parking lot entrance and exit processes, is designed. The main reasons for choosing this systemare that it greatly reduces the workload and contributes to the acceleration of operation in areas with heavytraffic. This work has been programmed with image processing methods on MATLAB and will take an imageinput with an internal or external camera system and give an output to the user. When we look at all thecountries of the world, it is observed that there are different shapes and sizes on the license plates. In this study,simple but effective morphological operations and Sobel edge detection method were used so that thesedifferences do not cause the system to make any mistakes. With this approach, the letters and numbers on theplate were divided by using the bounding box method, and an effective study was carried out. After this study,the template matching approach is used to identify the characters.

The object of research is a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network. Within the recent few decades, fires are considered as one of the most serious disaster that occurs in many places around the world. The severity of fire incidents causes damages to buildings, infrastructures and properties. Resulting losses of human’s life and costs them a lot of losses. Thus, fire poses a great threat to us significantly; it is extremely dangerous for fire fighters. Fires can be resulted by materials such as rubber and chemical products. Other sources of fire are the short circuits on electrical devices and faults in power circuits. Additionally, overheating and overloading problems can be the cause of fire incidents. All these reasons lead to bad consequences when there is no immediate response to such problems. The advent of computer vision technology has played such a significant role for human life. Artificial intelligence field has improved the efficiency and behaviors of robotics beyond expectations. The interference of artificial intelligence made robotics act intelligently. For this reason, in this paper we presented a mobile robot based on deep learning to detect the fire source and determines its coordinate position then automatically moves toward the target and extinguish fire. Deep learning algorithms are the efficient ones for object detection applications. CNN model is one of the most common deep learning algorithms which have been used in the study for the fire detection. Due to the insufficient amount of datasets and large efforts required to build model from scratch. MobileNet V2 is one of the CNN models that support transfer learning technique. After training the model and testing it on 20 % of the used datasets the classification accuracy achieved up to 98.01 %. The motion repeatability of the robot has been implemented and tested resulting mean error 0.648 cm., Об’єктом дослідження є прототип мобільного робота для виявлення пожежі та автоматичного пожежогасіння на основі згорткової нейронної мережі. За останні кілька десятиліть пожежі вважаються однією з найсерйозніших катастроф, які трапляються в багатьох місцях по всьому світу. Великі пожежі завдають шкоди будівлям, інфраструктурі та майну. Вони призводить до людських втрат і приносять людям великих збитків. Таким чином, вогонь становить для нас велику загрозу, він також надзвичайно небезпечний для пожежників. Пожежу можуть спричинити такі матеріали, як гума та хімічні продукти. Іншими джерелами пожежі є короткі замикання електрообладнання та несправності в електромережах. Крім того, причиною пожежі можуть бути проблеми з перегріванням і перевантаженням. Всі ці причини призводять до поганих наслідків, коли на такі проблеми не реагують негайно. Поява технології комп’ютерного зору відіграла таку значну роль у житті людини. Сфера штучного інтелекту покращила ефективність і поведінку робототехніки понад очікування. Втручання штучного інтелекту змусило робототехніку діяти розумно. З цієї причини в цій роботі представлено мобільного робота, заснованого на глибокому навчанні, для виявлення джерела пожежі та визначення його координатної позиції, а потім автоматичного руху до цілі та гасіння пожежі. Алгоритми глибокого навчання є ефективними для програм виявлення об’єктів. Модель CNN є одним із найпоширеніших алгоритмів глибокого навчання, який використовувався в дослідженні для виявлення пожежі. Через недостатню кількість наборів даних і великі зусилля, необхідні для створення моделі з нуля. MobileNet V2 – це одна з моделей CNN, що підтримує метод трансферного навчання. Після навчання моделі та її тестування на 20% використаних наборів даних точність класифікації досягла 98,01%. Повторюваність руху робота була реалізована та протестована, що дало середню похибку 0,648см.

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.

In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions., Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), main and suppl. material

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different up-sampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods.

Delta Robot is taking an important place in industry 4.0. Its ability to work fast and with precision is the reason why this robot now is used worldwide in the industrial field. Delta robots are mostly used for packaging, 3D printing, pick and place, etc. This robot can be controlled using programmable logic controller (PLC) devices. In recent years, image processing methods have been widely used in many robot applications. In this study, it is aimed to ensure that the robot successfully moves to the determined points by using image processing methods. Images for this study were obtained by attaching a camera to the robot system. In this study, the aim is to set the robot to move based on the visual data that is taken by the camera. The coordinates of each object are defined after several steps in image processing. To be able to move the robot to the desired coordinates, inverse and forward kinematic analysis is proposed. The obtained values are transferred to PLC using OPC. The necessary coding in TIA Portal is done to control the motor drivers so that the three stepper motors can ensure the delta robot moves to the desired coordinate position.

In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique, called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in dense forest and require real-time operation, we evaluate runtime vs. quality of these methods. Furthermore, we show that color anomaly detection methods that normally operate in the visual range always benefit from an additional far infrared (thermal) channel. We also show that, even without additional thermal bands, the choice of color space in the visual range already has an impact on the detection results. Color spaces like HSV and HLS have the potential to outperform the widely used RGB color space, especially when color anomaly detection is used for forest-like environments., Comment: 12 pages, 6 figures, 3 tables

Objectives Evaluation and follow-up of idiopathic pulmonary fibrosis (IPF) mainly rely on high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs). The elastic registration technique can quantitatively assess lung shrinkage. We aimed to investigate the correlation between lung shrinkage and morphological and functional deterioration in IPF. Methods Patients with IPF who underwent at least two HRCT scans and PFTs were retrospectively included. Elastic registration was performed on the baseline and follow-up HRCTs to obtain deformation maps of the whole lung. Jacobian determinants were calculated from the deformation fields and after logarithm transformation, log_jac values were represented on color maps to describe morphological deterioration, and to assess the correlation between log_jac values and PFTs. Results A total of 69 patients with IPF (male 66) were included. Jacobian maps demonstrated constriction of the lung parenchyma marked at the lung base in patients who were deteriorated on visual and PFT assessment. The log_jac values were significantly reduced in the deteriorated patients compared to the stable patients. Mean log_jac values showed positive correlation with baseline percentage of predicted vital capacity (VC%) (r = 0.394, p < 0.05) and percentage of predicted forced vital capacity (FVC%) (r = 0.395, p < 0.05). Additionally, the mean log_jac values were positively correlated with pulmonary vascular volume (r = 0.438, p < 0.01) and the number of pulmonary vascular branches (r = 0.326, p < 0.01). Conclusions Elastic registration between baseline and follow-up HRCT was helpful to quantitatively assess the morphological deterioration of lung shrinkage in IPF, and the quantitative indicator log_jac values were significantly correlated with PFTs. Key Points • The elastic registration on HRCT was helpful to quantitatively assess the deterioration of IPF. • Jacobian logarithm was significantly reduced in deteriorated patients and mean log_jac values were correlated with PFTs. • The mean log_jac values were related to the changes of pulmonary vascular volume and the number of vascular branches.

We propose using handcrafted features extracted from small datasets to classify the conditions of the construction materials. We hypothesize that features such as the color, roughness, and reflectance of a material surface can be used to identify details of the material. To test the hypothesis, we have developed a pre-trained model to classify material conditions based on reflectance, roughness and color features extracted from image data collected in a controlled (lab) environment. The knowledge learned in the pre-trained model is finally transferred to classify material conditions from a construction site (i.e., an uncontrolled environment). To demonstrate the proposed method, 80 data points were produced from the images collected under a controlled environment and used to develop a pre-trained model. The pre-trained model was re-trained to adapt to the real construction environment using 33 new data points generated through a separate process using images collected from a construction site. The pre-trained model achieved 93%; after retraining the model with the data from the actual site, the accuracy had a small decrease as expected, but still was promising with an 83% accuracy.

Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the Petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to significantly outperform CLEAN thanks to their ability to inject complex image models to regularize the inverse problem for image formation from visibility data. They were also shown to be parallelizable over large data volumes thanks to a splitting functionality enabling the decomposition of the data into blocks, for parallel processing of block-specific data-fidelity terms involved in the objective function. Focusing on intensity imaging, the splitting functionality is further exploited in this work to decompose the image cube into spatio-spectral facets, and enable parallel processing of facet-specific regularization terms in the objective function, leading to the "Faceted HyperSARA" algorithm. Reliable heuristics enabling an automatic setting of the regularization parameters involved in the objective are also introduced, based on estimates of the noise level, transferred from the visibility domain to the domains where the regularization is applied. Simulation results based on a MATLAB implementation and involving synthetic image cubes and data close to Gigabyte size confirm that faceting can provide a major increase in parallelization capability when compared to the non-faceted approach (HyperSARA)., To appear in MNRAS

Tensor fields are useful for modeling the structure of biological tissues. The challenge to measure tensor fields involves acquiring sufficient data of scalar measurements that are physically achievable and reconstructing tensors from as few projections as possible for efficient applications in medical imaging. In this paper, we present a filtered back-projection algorithm for the reconstruction of a symmetric second-rank tensor field from directional X-ray projections about three axes. The tensor field is decomposed into a solenoidal and irrotational component, each of three unknowns. Using the Fourier projection theorem, a filtered back-projection algorithm is derived to reconstruct the solenoidal and irrotational components from projections acquired around three axes. A simple illustrative phantom consisting of two spherical shells and a 3D digital cardiac diffusion image obtained from diffusion tensor MRI of an excised human heart are used to simulate directional X-ray projections. The simulations validate the mathematical derivations and demonstrate reasonable noise properties of the algorithm. The decomposition of the tensor field into solenoidal and irrotational components provides insight into the development of algorithms for reconstructing tensor fields with sufficient samples in terms of the type of directional projections and the necessary orbits for the acquisition of the projections of the tensor field.

A computational method for matched filtration with analytical profile of the blurred digital image of the investigated objects on digital frames has been developed. Such «blurred» objects can be the result of an involuntary shift of a fixed camera, an incorrect choice of the mode of guiding the telescope (diurnal or object tracking) or a failure of the diurnal tracking. This computational method is based on the analytical selection of the typical form of the object’s image, as well as on the choice of special parameters for the transfer function of the matched filter for the blurred digital image, which makes it possible to evaluate the required parameters of the blurred digital image. In addition, determining the number of Gaussians of the object’s image makes it possible to perform the most accurate assessment of the initial approximation of the parameters of their shape. Thus, matched filtration makes it possible to highlight the investigated objects with a blurred image of a typical shape against the background of substrate noise. Using the computational method of matched filtration makes it possible to improve the segmentation of images of reference objects on the frame and reduce the number of false detections. The developed computational method for matched filtration with analytical profile of the blurred digital image of the investigated objects on the frames was tested in practice as part of the research of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software for the operational automated detection of new and observation of known objects with a weak brightness. Owing to the Lemur software using and the proposed computational method introduced into it, more than 500,000 measurements of the various investigated objects were successfully processed and identified.