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Data augmentation improves machine learning model performance by diversifying training data. Initially, manual selection of augmentation techniques was required; however, AutoAugment automated this process. However, recent studies identified limits in the search space of AutoAugment, which hindered performance gains. In this study, we introduced a novel method called Blend AutoAugment that utilizes linear blending with random weights to enhance the performance of image classification in deep learning. Our method consists of two stages: 1) policy search and 2) image blending. In the policy search stage, optimal policies are determined by employing reinforcement learning to explore a search space, comprising the types of operations, their application probabilities, and their magnitudes. In the image blending stage, multiple optimal policies discovered in the previous stage are selected to generate augmented images, and the generated images are blended using random weights. Performance comparison experiments were carried out using the Wide-ResNet40-2 and Wide-ResNet28-10 models on the CIFAR-10, CIFAR-100, CIFAR-10-C, and CIFAR-100-C datasets. In addition, the proposed method was compared with existing methods. Notably, the proposed method exhibited higher accuracy and robustness than those of existing methods. Furthermore, it was applicable to various existing automatic data augmentation methods. In summary, this study introduced an advanced automatic data augmentation method that expands the previously limited search space and utilizes image blending to achieve better accuracy and robustness than conventional methods. Our source code is available at https://github.com/Sarrzae/BAA.

Hemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to rest of the body tissues. Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition characterized by a deficiency of red blood cells. This measurement is particularly vital before initiating blood transfusions for thalassemia patients. Non-invasive estimation of hemoglobin levels can be achieved through photoplethysmography (PPG)-based methods. PPG is an optical method to measure blood volume changes in successive heart beats. PPG signals can be obtained from fingertip videos using a light source and a photodetector. SmartphonePPG utilizes a smartphone’s flashlight as a light source and its camera as a photodetector to acquire PPG signals, offering an affordable and portable point-of-care tool. Despite the ubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG characteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses a significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset comprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone’s camera. The dataset, publicly accessible for research purposes (https://forms.gle/LB4qn81ZMqEuy3V27), covers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL - 12.4 gm/dL). Utilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to estimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection from PPG signals, overcoming a limitation in existing smartphone PPG-based hemoglobin estimation systems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared with the gold standard laboratory method, Complete Blood Count (CBC) test reports. In contrast, HemaApp, a state-of-the-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on our dataset. The accuracy and simplicity of our model position it as a promising alternative to existing non-invasive hemoglobin level estimation methods.

Breast cancer is the most commonly diagnosed cancer among women, globally. The occurrence and fatality rates are high for breast cancer compared to other types of cancer. The World Cancer report 2020 points out early detection and rapid treatment as the most efficient intervention to control this malignancy. Histopathological image analysis has great significance in early diagnosis of the disease. Our work has significant biological and medical potential for automatically processing different histopathology images to identify breast cancer and its corresponding grade. Unlike the existing models, we grade breast cancer by including both local and global features. The proposed model is a hybrid multi-class classification model using depth-wise separable convolutional networks and transformers, where both local and global features are considered. In order to resolve the self-attention module complexity in transformers patch merging is performed. The proposed model can classify pathological images of public breast cancer data sets into different categories. The model was evaluated on three publicly available datasets, like BACH, BreakHis and IDC. The accuracy of the proposed model is 97.800 % on the BACH dataset, 98.130 % on BreakHis dataset and 98.320 % for the IDC dataset.

This study aims addresses the challenge of gesture recognition in smart sports stadiums through the fusion enhancement of YOLOv5 and Copula Bayesian Classifier algorithm. By leveraging FasterNet for YOLOv5 transformation, integrating the Convolutional Block Attention Module (CBAM) as an attention mechanism, and merging it with the Copula Bayesian Classifier, we propose a tandem model for robust gesture recognition in the complex environments of sports venues. Extensive training on a substantial dataset yields an efficient and accurate gesture recognition model. Our findings showcase the effectiveness of the proposed algorithm, achieving a remarkable accuracy rate of 99.2% in identifying gesture categories swiftly. This advancement holds significant implications for future human-computer interaction in smart sports stadiums, offering a more seamless and intelligent mode of interaction for both spectators and athletes. Ultimately, it enhances user experience and management efficiency within smart sports venues.

In its long history, chum salmon has been propagated by hatching and stocking. Age assessment is necessary to monitor the migration status of chum salmon, and it is desirable to increase the number of assessments in order to obtain precise and accurate knowledge about salmon as a resource. This study introduced an automated age assessment system that uses information technologies, such as artificial neural networks and image processing, to improve efficiency compared to manual assessment. Specifically, we first developed a method for extracting scale regions from scale replica sample images and creating a database. We then used a resting zone detection technique based on semantic segmentation, allowing us to automate evaluation using scaled images as part of the age assessment method. Age assessment from resting zone images was realized by an image processing method based on the skills of the staff of the Japanese Fisheries Research Institute, such as an age counting method using circular structures. Experimental results show that our proposed method outperforms other approaches; salmon age assessment was accurate to within an error of 0.5 years. Our method encompasses automatic data processing of scale images, resting zone detection, and age assessment, and will contribute to the efficiency of the Fisheries Research Institute in terms of both work efficiency and salmon data research.

Against the backdrop of the pervasive global challenge of cancer, with particular emphasis on lung cancer (LC), this study centers its investigation on the critical realm of early detection leveraging artificial intelligence (AI) within the domain of histological image analysis. Through the fusion of DenseNet201 with color histogram techniques, a novel hybrid feature set emerges, engineered to elevate classification accuracy. The comprehensive evaluation encompasses eight diverse machine learning (ML) algorithms, spanning from K-Nearest Neighbors (KNN) to Support Vector Machines (SVM), including notable contenders such as LightGBM (LGBM), CatBoost, XGBoost, decision trees (DT), random forests (RF), and multinomial naive Bayes (MultinomialNB). This rigorous examination illuminates a distinguished model, achieving a remarkable accuracy rate of 99.683% on the LC25000 dataset. The extension of this methodology to breast cancer detection, utilizing the BreakHis dataset, yields a commendable accuracy rate of 94.808%. These findings underscore the transformative potential of AI in the intricate landscape of histopathological analysis, positioning it as a pivotal force in advancing diagnostic capabilities. A meticulous comparative analysis not only underscores the merits but also elucidates the limitations of existing AI applications in medical imaging, thereby charting a roadmap for future refinements and clinical deployments. Consequently, continued research in AI within clinical settings is advocated, with the ultimate aim of fortifying early cancer diagnosis and subsequently enhancing patient outcomes through judicious therapeutic interventions.

People are preoccupied with their own duties in today’s environment, making it tough to oversee and care for disabled people. Paralyzed and disabled people, on the other hand, have a powerful desire to move around freely. Different initiatives have been taken in the past to improve and preserve the self-esteem of such people, and various technologies have also been created to assist them in a better way. The primary advantage of a vision-based autonomous wheelchair is that it can provide greater independence to users with limited mobility. The methodology of other smart wheelchairs varies depending on the specific technology used. Some smart wheelchairs may use sensors to detect obstacles and provide feedback to the user, while others may incorporate GPS and other navigation technologies to assist with indoor and outdoor navigation. Our study proposes a revolutionary implementation of an autonomous system for fully handicapped people, allowing them to drive wheelchairs using just their eyes. The wheelchair’s orientation will be determined by the orientation of the eye. The camera will follow the movement of the eyes. Furthermore, sensors will be installed on the front side of the wheelchair to identify any obstacles along its path. The calibration of the camera with the human eye, without obstructing its vision, is the most challenging task in this research, besides controlling the wheels of the wheelchair.

Luojia 3-01 is the world's first intelligent remote sensing satellite, equipped with various imaging modes including video, frame-pushing, and scan-pushing. It boasts submeter level multimode optical imaging capabilities, on-orbit intelligent processing, and real-time intra-satellite and satellite-to-ground data transmission. Throughout the satellite's design and development, we have established the real-time intelligent information service architecture for Luojia 3-01, marking a new paradigm in on-orbit processing and real-time services for intelligent remote sensing satellites. We have proposed the on-orbit real-time processing architecture of Luojia 3-01, addressing the challenges of limited computational and storage resources, particularly in on-orbit processing of vast volumes of remote sensing data, including core algorithmic bottlenecks in “correction-extraction-compression.” A novel intelligent remote sensing satellite system has been developed, featuring multimode imaging, an open platform, intelligent processing, and satellite-to-ground interconnectivity, which significantly reduces the response time of remote sensing services to less than 8 min, thereby shortening the cycle from data acquisition to intelligent information service. This innovation spearheads a technological leap in remote sensing satellite services from data to information, from post-event to real-time, and from professional to widespread public applications, laying a solid foundation for the popularization and commercialization of China's intelligent remote sensing satellites.

Masks play a crucial role in preventing respiratory diseases and have diverse applications in national public health and industrial safety. Efficient mask-wearing detection systems are essential for ensuring accuracy and real-time performance. To overcome the challenges of extensive model calculations, extensive parameter volume, and complex hardware deployment in the current mask-wearing detection system, a lightweight mask detection model with improved YOLOv5 is proposed. Firstly, this study proposes a new lightweight network—EMA-FasterNet, as the backbone network of YOLOv5, which reduces the computation while preserving the information of each channel. Secondly, using Depthwise Separable Convolutions (DepthSepConv) to replace some C3 modules in the Neck further compresses the model volume, parameters, and computation. Finally, to prevent the detection of missing objects due to the removal of overlapping candidate boxes, use Soft Non-Maximum Suppression (NMS) to replace NMS. Compared with YOLOv5s, the proposed YOLOv5-S2C2 has high mAP in both Dataset I and Dataset II, while the parameters and computation of the model are reduced by about 56% and 57.0%, respectively. The volume is about 44% of the original model and the inference speed on GPU is improved by about 30%. These improvements show that the proposed model achieves a lightweight design and excellent real-time performance. It is also well suited for efficient use on hardware with limited computational resources.

Agriculture is the ultimate imperative and primary source of origin to furnish domestic income for multifarious countries. The disease caused in plants due to various pathogens like viruses, fungi, and bacteria is liable for considerable monetary losses in the agriculture corporation across the world. The security of crops concerning quality and quantity is crucial to monitor disease in plants. Thus, recognition of plant disease is essential. The plant disease syndrome is noticeable in distinct parts of plants. Nonetheless, commonly the infection is detected in distinct leaves of plants. Computer vision, deep learning, few-shot learning, and soft computing techniques are utilized by various investigators to automatically identify the disease in plants via leaf images. These techniques also benefit farmers in achieving expeditious and appropriate actions to avoid a reduction in the quality and quantity of crops. The application of these techniques in the recognition of disease can avert the disadvantage of origin by a factious selection of disease features, extraction of features, and boost the speed of technology and efficiency of research. Also, certain molecular techniques have been established to prevent and mitigate the pathogenic threat. Hence, this review helps the investigator to automatically detect disease in plants using machine learning, deep learning and few shot learning and provide certain diagnosis techniques to prevent disease. Moreover, some of the future works in the classification of disease are also discussed.

Understanding liquid spray is essential for spray applications, including but not limited to designing fuel-efficient engines. Due to the challenges involved in collecting real-world liquid spray images, a synthetic liquid spray was generated using fluid simulation based on the atomization of the liquid jet. Semantic segmentation was chosen to analyze the liquid spray, as it reflects the precise location of the objects in the image. This paper presents a workflow to train a U-Net with a small sample (only 24 training images) dataset under the constraint that no ground truth is provided. An image is selected from the generated images of liquid spray and edited by randomly masking some objects. After the chosen image is annotated, a data augmentation technique that includes rotation and Gaussian smoothing is applied, resulting in 24 images available as the training set. The RGB original-sized images are fed to U-Net for training. Due to how the liquid spray images are obtained in the real world, Gaussian smoothing is explored as the inductive bias. Gaussian smoothing is incorporated between the convolutional layers of U-Net to enhance its feature extraction ability. The experiment results showed that the segmentation output improved when smoothing was incorporated into the U-Net. By visualizing the convolutional feature map of trained U-Net, we discover that smoothing makes convolution less biased to texture information. Going through this workflow, the trained U-Net is found to generalize well to the test images despite learning from few samples. Code is available at https://github.com/lynerlwl/spray-unet

To improve the effect of table tennis tactical analysis, this study proposed a table tennis video tactical analysis method based on image fuzzy edge recognition algorithm. This algorithm can effectively recognize edges in images to improve recognition accuracy and reduce errors and rejection rates. In addition, this research combines the methods of target tracking and trajectory estimation to build a table tennis video game tactical analysis platform. The experimental data show that the recognition accuracy of the proposed fuzzy edge recognition algorithm is 98.1%, and the error and rejection rates are less than 1.6%. Compared with the image edge detection algorithm based on interval value fuzzy, the proposed algorithm has better performance. When the positioning error is 10%, the precision of the table tennis target tracking algorithm can reach 94.3%, which is 9.3% higher than that of the direct linear transform algorithm. The table tennis video game tactical analysis platform can analyze the velocity of ping-pong ball according to the video, and records the droppoint of ping-pong ball, which helps the players to develop more effective tactics and strategies to improve their competitive level and achievements.

Acute lymphoblastic leukemia (ALL), one of the prevalent types of carcinogenic disease, has been seen a deadly illness exposing numerous patients across the world to potential threats of lives. It impacts both adults and children providing a narrow range of chances of being cured if diagnosed at a later stage. A definitive diagnosis often demands highly invasive diagnostic procedures thereby proving time-consuming and expensive. Peripheral Blood Smear (PBS) images have been playing a crucial role in the initial screening of ALL in suspected individuals. However, the nonspecific nature of ALL poses a substantial challenge in the analysis of these images thus leaving space for misdiagnosis. Aiming at contribute to the early diagnoses of this life-threatening disease, we put forward automated platform for screening the presence of ALL concerning its specific subtypes (benign, Early Pro-B, Pro-B and Pre-B) using PBS images. The proposed web based platform follows weighted ensemble learning technique using a Residual Convolutional Neural Network (ResNet-152) as the base learner to identify ALL from hematogone cases and then determine ALL subtypes. This is likely to save both diagnosis time and the efforts of clinicians and patients. Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). The promising results demonstrate that the proposed platform has the potential to be used as a reliable tool for early diagnosis of ALL and its sub-types. Furthermore, this provides references for pathologists and healthcare providers, aiding them in producing specific guidelines and more informed choices about patient and disease management.

With the continuous acceleration of urbanization, the parking problem is becoming increasingly serious. How to better manage parking resources has become an urgent problem to be solved in urban development. In this context, according to the historical data and real-time video data collected by the parking camera, this paper proposes an algorithm for parking space detection and state recognition. Through image preprocessing, region of interest selection, Hough line detection, and parking information recognition of the input test image, an intelligent parking space detection model is constructed, which improves the utilization rate of parking space and reduces the management cost. On this basis, according to the free parking space data obtained by the detection algorithm, a short-term demand prediction algorithm for on-road parking based on Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM) was proposed. Through the preprocessing of input parking space data, time vector transformation, data separation, model training, and prediction, the parking demand data is predicted and analyzed. By comparing the prediction results of multiple models, it was found that the CNN-LSTM prediction model had the best model stability and goodness of fit, the lowest Mean Error (MAE) and Root Mean Square Error (RMSE), the errors of working days were 13.301 and 21.156, and the errors of rest days were 12.573 and 20.739, respectively. It shows that CNN-LSTM can effectively capture the time and spatial feature information of parking lot free parking space data, and the prediction accuracy is good, which can be used to predict the number of free parking spaces in parking lots.

Real-time Monte Carlo denoising aims to denoise a 1spp-rendered image with a limited time budget. Many latest techniques for real-time Monte Carlo denoising utilize temporal accumulation (TA) as a pre-processing to improve the temporal stability of successive frames and increase the effective spp. However, existing techniques using TA used to suffer from significant performance degradation when TA does not work well. In addition, they have the disadvantage of deteriorating performance in dynamic scenes because pixel information of the current frame cannot be sufficiently utilized due to the pixel averaging effect between temporally adjacent frames. To solve this problem, this paper proposes a framework that utilizes both 1spp images and temporally accumulated 1spp (TA-1spp) images. First, the multi-scale kernel prediction module estimates kernel maps for filtering 1spp images and TA-1spp images, respectively. Then, the filtered images are properly fused so that the two advantages of 1spp and TA-1spp images can create synergy. Also, the remaining noise is removed through the refinement module and fine details are reconstructed to improve the model flexibility, beyond using only the kernel prediction module. As a result, we achieve better quantitative and qualitative performance at 39% faster than state-of-the-art (SOTA) real-time Monte Carlo denoisers.

Pomegranates are nutrient-rich fruits renowned for their vibrant ruby-red seeds and antioxidant properties. With a rich history rooted in various cultures, pomegranates have gained widespread popularity for their distinct flavor and potential health benefits. Timely detection and understanding of the growth stages of pomegranates can facilitate optimized resource allocation, targeted interventions, and efficient crop management. Additionally, early detection contributes to maximizing crop yield, ensuring product quality, and mitigating potential risks such as diseases and pest infestations. The primary goal of the present study is the early detection of pomegranate growth stages using an efficient approach. We conducted our experiments using standard image data of the pomegranate growth stages, comprising 5857 files categorized into five classes: Bud, Early-Fruit, Flower, Mid-growth, and Ripe. We propose a transfer learning-based CRnet approach to capture spatial features from pomegranate images depicting the five stages of pomegranate growth. The extracted spatial features serve as inputs for the random forest method, resulting in the creation of a new probabilistic feature set. These new probabilistic features assist the proposed model in performing the detection of pomegranate growth stages. To evaluate performance, we implemented state-of-the-art image classification techniques, including a Convolutional Neural Network (CNN), K-Neighbors Classifier (KNC), Gaussian Naive Bayes (GNB), and Logistic Regression (LR). To ensure the accuracy of applied machine learning methods, we utilized a hyperparameter optimization approach and a k-fold-based cross-validation technique. Additionally, computational complexity is determined. Extensive analysis of research results shows that by using the proposed features, the random forest model outperformed state-of-the-art methods with a high accuracy of 98% in predicting pomegranate growth stages. Our proposed scheme has the potential for the timely detection of pomegranate growth stages, assisting farmers in maximizing crop yield and mitigating potential risks.

The most unexpected and fatal kind of cancer, melanoma, has been on the rise in its spread to different parts of the body. Early detection can significantly reduce its fatality rate. The primary stages of melanoma for its identification by the unaided eye are a difficult task that demands extensive training and understanding. Due to a lack of qualified dermatologists, a computerized and automated method is required to correctly detect melanoma. This study achieved this feat through a proposed system that can effectively detect and classify melanoma as benign or malignant. The process begins with image template matching by using normalized cross-correlation technique induction to mark the infected area of skin lesion as the region of interest (ROI) from ISIC datasets 2017, 2019, and 2020 dermoscopic images. Our novel model dynamically calculated number of clusters is assigned to the $k$ -means clustering algorithm, and ROI is extracted. Histogram equalization is applied to the output image for contrast enhancement. Hu Moment method is implemented for shape classification and part recognition from the segmented image. GLCM-based Haralick feature extractor is used in the proposed system to extract textural features generating the feature vector from the segmented skin lesion. It leads the classifier to identify skin lesions as cancerous or non-cancerous. Random Forest, Decision Tree, Support Vector Machine (SVM), Gaussian Naïve Bayes, Logistic Regression, and K-nearest neighbor (KNN) Classifiers are used for classification. The KNN, SVM, and Random Forest was successful in attaining the highest accuracy of 99.29%, 99.38%, and 99.46% on the given dermoscopic images datasets ISIC-2019, ISIC-2020 and ISIC-2017 respectively. The proposed method, the normalized cross correlation-based k-means clustering model, was found to be more robust and accurate than existing methods and incorporates much more feature information from the images.

Current carotid atherosclerosis diagnostic protocols do not feature techniques that would allow for early or frequent medical examinations, leaving a significant number of asymptomatic carotid stenosis cases undetected and often leading to strokes. The key challenge is that current diagnostics are highly operator-dependent. In this work we used idealised biological models to demonstrate a new rapid, potentially inexpensive and operator-independent diagnostic method, aimed at detecting whether a stenosis exists, rather than seeking to be accurately quantifying or localising it. An array of electrodes was used to obtain sequential bioimpedance values over the skin, through a novel scanning technique, covering an area over the artery of interest. FEM simulations, verified through in-vitro experiments on gelatine phantoms, were used to validate the method. The final results, obtained through image processing algorithms, were in the form of planar bio-impedance maps and were successful both in identifying arterial features and detecting the presence of stenoses of different sizes, and the overall accuracy of detecting stenoses in a vessel without bifurcation is 97.78% and with bifurcation is 91.11%. The results could also be used to indicate the artery’s relative orientation to the sensor, eliminating the need for manual alignment by a specialist operator. Therefore, this method shows promise for routine medical examination, either in primary care, or even at home, to indicate whether a patient would require further, more detailed examinations at a specialist clinic.

Dorsal hand vein (DHV) recognition is a burgeoning biometric technology that has recently garnered considerable attention. This article uses image processing and deep learning to present a novel DHV recognition approach. It involves detecting and identifying the unique patterns present in the DHV. The proposed system begins with the preprocessing mechanism that is applied to enhance the quality of the acquired images, including contrast enhancement and noise reduction, by using some filters such as Median and Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, a deep learning model, such as a convolutional neural network (CNN), is employed to automatically abstract discriminative features from the preprocessed vein images. The empirical outcomes prove the influence and reliability of the proposed technique for vein recognition, making it a promising solution for biometric authentication systems. Compared with traditional CNN, the proposed approach shows good accuracy and classification rate results. The suggested model achieved a high recognition rate accuracy, recall, precious, and f-score of 99.7%,97%,96%, and 96%, respectively, and a recognition time of about 1283.45 s. To enrich the model’s capability for feature recognition and reduce recognition time, decrease the intricacy of learning and the connectivity CNN structure, an alternative approach based on Restricted Boltzmann Machines (RBM) was assessed. This strategy exhibits superior accuracy in comparison to other contemporary algorithms. The proposed RBM achieved a high recognition rate accuracy, recall, precious, and f-score of 99.9%,99%,99%, and 99%, respectively, and a recognition time of about 137.235s.

Recent advancements in image processing and computer vision have brought significant transformations in healthcare technology, leading to significant improvements in diagnosis accuracy, cost-effectiveness, and time efficiency. Magnetic Resonance Imaging (MRI) is employed by the radiologist for its remarkable ability to detect even the most subtle brain abnormalities. This study considers a comprehensive analysis of the two prominent object identification frameworks, YOLOv5 and YOLOv7, leveraging state-of-the-art deep learning architectures to classify and detect brain cancers within MRI. The brain tumor dataset encompasses three distinct classes, including meningiomas, gliomas and pituitary tumors. To ensure precise segmentation of the tumor regions, the preprocessing phase incorporates advanced mask alignment techniques. This preprocessed dataset has been used to evaluate the performance of the deep learning models for brain tumor detection and classification. From the numerical results of YOLOv5, it was noticed that a recall score of 0.905 for box detection and 0.906 for mask segmentation, with a precision score of 0.94 and 0.936, respectively. At an IoU threshold of 0.5, both box detection and mask segmentation achieve a mAP of 0.947, whereas, at an IoU threshold of 0.5 to 0.95, they achieve mAPs of 0.666 and 0.657, respectively. In comparison, YOLOv7 exhibits strong performance with box detection accuracy of 0.936 and a mask segmentation accuracy of 0.935. The recall score are 0.904 for box detection and mask segmentation is 0.903. Notably, the mAP result at the IoU threshold of 0.5 are 0.94 for box detection and mask segmentation is 0.941. Over the broader IoU spectrum of 0.5 to 0.95, the mAP was 0.677 for box detection and 0.659 for mask segmentation. To underscore the novelty of the approach, the performance of the proposed framework is systematically compared with established methods such as RCNN, Faster RCNN, and Mask RCNN.

Driver assistance systems and autonomous vehicle navigation have become important topics in vehicular technology. Among all of the functions, lane detection is one of the most important. A variety of approaches have been proposed in this field. While learning-based methods achieve impressive accuracy in detecting complex lane markings under clear daylight conditions, adapting these models to diverse weather conditions remains a challenge. On the other hand, geometric-based approaches require parameter tuning for different scenarios but require fewer computational resources. A self-tuned algorithm with high generalizability across diverse weather conditions is proposed in this paper. The algorithm integrates fuzzy logic-based adaptive functions with edge identification and line detection modules, enabling image adjustments in response to challenging weather conditions. The proposed tracking function utilizes previous detection results to fine-tune the selected Range of Interest (ROI), optimizing both accuracy and processing time. By incorporating these adaptive features into common geometric-based frameworks, the algorithm achieves higher detection rates compared to previous studies during challenging weather conditions. Furthermore, the proposed work exhibits better generalizability and significantly shorter processing time when compared to state-of-the-art learning-based models, as demonstrated through extensive testing on multiple datasets.

iquaflow is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, iquaflow measures quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem involves finding images that, while being compressed to their smallest possible file size, still maintain sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with iquaflow is suitable for such case. All this development is wrapped in Mlflow: an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookiecutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub.

Deep learning models learn powerful representational spaces required for handling complex tasks. Recently, data augmentation techniques, region-level, and image-level augmentation have proved effective in significantly improving deep learning models’ generalization performance. Nevertheless, such methods may lose some critical features or are still computationally heavy (or inefficient) due to additional computation burdens. To address this issue, in this paper, we present a novel unified data augmentation method for deep learning models, namely, RandMixAugment, which effectively combines the intrinsic properties of region-level augmentation and image-level augmentation. Specifically, the proposed RandMixAugment employs automated augmentation with masking and mixing operations. Experiments are conducted on well-known CIFAR datasets (CIFAR-10 and CIFAR-100) to verify the effectiveness of the proposed scheme compared to state-of-the-art augmentation techniques. The experimental results demonstrate that the proposed RandMixAugment yields superior performance over state-of-the-art techniques on image classification tasks and further improves the performance of the baseline deep learning model by 1.2% and 2.4% on CIFAR-10 and CIFAR-100 datasets, respectively.

Filtering is a fundamental tool in image processing, and its acceleration affects many applications. Therefore, various algorithmic and hardware accelerations have been proposed for filtering. Recursive processing using infinite impulse response (IIR) filtering is an efficient algorithm, and various hardware acceleration methods have been applied to IIR filtering. In addition, a domain-specific language (DSL) of RecFilter was proposed to generate efficient IIR code for various hardware applications as an extension of image processing language, Halide. Recursive filters based on sliding discrete cosine transform (SDCT) have been the most efficient approximations in recent years. For hardware acceleration, parallelization of recursive filters is challenging. One of the most efficient methods is tile-based parallelization. However, even if a function is optimized and modularized, it is not sufficiently optimized for applications where various pre/post-processing steps are coupled before and after filtering. Additionally, multiplatform deployment requires reimplementation of the code. In this study, we extended Halide for SDCT convolutions to realize efficient computing of image processing applications with filtering, named SlidingConv. The experimental results showed that SlidingConv is faster than the hand-tuned CPU code and 1/1900 of the hand-tuned code length, running more efficiently than de facto libraries like OpenCV. To verify its efficiency, we deployed the code on various hardware (x86/64 CPU with AVX2/AVX-512, ARM CPU, and GPU). In addition, we verified that the proposed method can accelerate image processing with pre/post-processing for filtering. Our code is available at https://fukushimalab.github.io/SlidingConv/.

The conventional clustering methods assume a binary classification and establish a complete inclusive or exclusive type relation of an object with a cluster. In contrast, a three-way paradigm handles situations where an object may or may not belong to a cluster, i.e., uncertain. The objects belonging to the uncertainty region may lead to inclusion or exclusion after further processing and information. One of the use cases of the three-way paradigm is the overlapping region between different clusters. Effective computation of overlapping objects is crucial to the application’s overall success. In this paper, we employ a three-way clustering approach inspired by image blurring and sharpening operations that consider the objects in the inside or outside regions of a cluster to be non-overlapping. The objects belonging to the partial region of more than one cluster are considered overlapping. The experiment conducted on Birds, Scenes, and 20 newsgroups datasets indicates that the proposed approach improves the F1 measure and hamming loss up to 18.6% and 4.9%, respectively. Furthermore, the system’s robustness for overlapping regions is observed using typical clustering measures. The experimental results suggested that the proposed approach may improve the computation of overlapping regions effectively.

In histopathological image analysis, the feature extraction task for classification proves to be demanding. This difficulty arises from the assortment of histological features appropriate for individual problems and the intricate presence of diverse geometric structures. The method proposed in this study leverages dictionary learning and sparse coding techniques to create priors tailored to specific targets, which is essential for classification purposes. Our approach introduces Penalized Sequential Discriminative Dictionary Learning (PSDDL), designed to integrate histopathological image features by acquiring structured, class-specific dictionaries. Initially, PSDDL constructs a dictionary from the input data, incorporating label information for each class. Subsequently, the proposed algorithm introduces a penalty and regularization term to amplify the efficacy of the acquired dictionary. Furthermore, the proposed method also tackles the class imbalance in the dataset by leveraging dictionary learning. Mainly, under-sampling is performed on the dataset, and the number of samples of fewer classes is kept for all categories and passed to the dictionary learning algorithm. Extensive experimental results highlight a notable enhancement in classification performance. The proposed structured discriminative dictionary learning technique consistently produces improved accuracies compared to other contemporary methods for classifying the BreakHis breast cancer dataset. Integrating class-specific information into the process of dictionary learning paves the way for enriching the interpretive capabilities of machine learning models while delving deeper into our comprehension of the complex and intricate structures inherent in biological tissues.

Infrared imaging has broad and important applications. However, the infrared detector manufacture technique limits the detector resolution and the resolution of infrared images. In this work, we design a Recurrent Large Kernel Attention Neural Network (RLKA-Net) for single infrared image super-resolution(SR), and then demonstrate its superior performance. Compared to other SR networks, RLKA-Net is a lightweight network capable of extracting spatial and temporal features from infrared images. To extract spatial features, we use multiple stacked Recurrent Learning Units (RLUs) to expand the network’s receptive field, while the large kernel attention mechanism in RLUs is used to obtain attention maps at various granularity. To extract temporal features, RLKA-Net uses the recurrent learning strategy to keep persistent memory of extracted features, which contribute to more precise reconstruction results. Moreover, RLKA-Net employs an Attention Gate (AG) to reduce the number of parameters and expedite the training process. We demonstrate the efficacy of the Recurrent Learning Stages (RLS), Large Kernel Attention Block (LKAB), and Attention Gate mechanisms through ablation studies. We test RLKA-Net on several infrared image datasets. The experimental results demonstrate that RLKA-Net presents state-of-the-art performance compared to existing SR models. The code and models are available at https://github.com/ZedFm/ RLKA-Net.

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects’ excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments. [ABSTRACT FROM AUTHOR]

Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches. [ABSTRACT FROM AUTHOR]

We present our automated real-time socket inspection system capable of detecting an assortment of defects including metallic and liquid staining, loose capacitors and pins, and other debris and foreign material (FM). Our test tools pick and place manufactured units into sockets for electrical testing. Any debris accumulated inside the test sockets will likely damage subsequent units until the defective socket is replaced. To quickly capture in-situ defects and mitigate further damage, we equipped each pick-and-place arm with a new vision system designed to fit within the existing tool. The tight footprint constraints required a highly compact imaging system which resulted in a variety of image artifacts, creating several unique challenges. Our inspection algorithm utilizes a variety of advanced computer vision and machine learning techniques to normalize images, extract and match features, register the images, suppress unwanted artifacts, and detect defects. The detected changes are then sent to a deep learning classifier to further filter between true defects and natural socket deterioration. The flagged socket images can be manually dispositioned by the user and the socket can be sent for repair or cleaning as needed. [ABSTRACT FROM AUTHOR]

An accuracy-configurable adder (ACA) meets various requirements in energy efficiency and accuracy of many applications. However, it suffers from problems such as a large hardware cost for performing configurations. In this article, we propose two energy-efficient transistor-level accuracy-configurable full adders, referred to as a positive ACA (PACA) and a negative ACA (NACA). Each design uses only two transistors as switches driven by an enable signal to configure the accurate and approximate modes. Dynamic accuracy-configurable adders (DACAs) are further developed by cascading the PACAs or NACAs. The DACA design realizes a remarkable trade-off between accuracy and performance by switching between different degrees of approximations. Simulation results show that compared with an accurate adder, a 16-bit DACA provides a saving of up to 43.61% in power and a reduction of 83.31% in delay, with only a loss of 11.96% in accuracy. The advantage of the DACA is illustrated by applications of image processing and classification using a neural network model. [ABSTRACT FROM AUTHOR]

In this article, an image processing-based localization system is developed for remote nondestructive evaluation of welds within industrial assets. Manual ultrasonic inspection of large-scale structures is often repetitive, time-consuming, and benefits greatly from robotic support, however, these robotic systems are often fixed to a single purpose, lack self-awareness of their surrounding environment, and can be limited to simple geometry. For the inspection of welds, which are often carried out using phased array ultrasonic testing, there is a reliance on the use of surface features for automated tracking such as the laser profiling of a weld cap. For the inspection of more complex geometry such as nonlinear or saddle welds, a more positionally sensitive method is required. The proposed system utilizes information already available to a nondestructive inspector in the form of live phased array ultrasonic images to estimate the location of the weld using nonsurface, volumetric data. Data is captured using a 64-element, 10-MHz phased array probe mounted to the end effector of a small robotic manipulator which increases the scope of applications due to its heightened flexibility when compared to on-the-market alternatives. Morphological operations are applied to the ultrasonic data to reduce the noise apparent from regions of parent material and promote the data reflected from grain boundaries within the weld material. Through a series of image processing techniques, it is possible to predict the position of a weld under inspection with an absolute mean positional error of $\mathrm {0.8 \text {m} \text { m} }$ . From this study, the localization system is to be embedded within a remote system for extensive data acquisition of welds on large structures.

Visual communication (VC) manages to demonstrate optimal perception effect through reasonable element planning. It has been a more prevalent tendency to introduce 3D image reconstruction into VC activities. Currently, there still lacks reliable solution for this purpose under VC scenes. To deal with this issue, we propose a Kruppa equation-based virtual reconstruction method for 3D images under VC scenes. Firstly, the feature descriptor procedure in scale-invariant feature transform (SIFT) algorithm is introduced into image matching. Then, Harris corner extraction (HCE) algorithm is utilized for selection of feature points. And according to HCE algorithm, we develop a specific direction feature description method. In the proposal, main camera points are calibrated separately, which not only makes the calibration accuracy of the main points higher, but also reduces the unknown parameters of Kruppa equation. Finally, we conduct some experiments to verify efficiency of the proposal. The obtained results show that calibration accuracy of the proposal is 12% higher than other comparison methods, and better virtual reconstruction results can be achieved.

The occurrence of cracks in brown rice kernels has a substantial impact on grain quality. The timely and accurate detection of rice grains with cracks is crucial for enhancing the overall quality and flavor of processed rice. In this study, we developed an optical observation platform and optimized the original ResNet-18 neural network structure to improve the detection and classification of grain cracks. We established image datasets for japonica and indica rice varieties, and employed image augmentation and model migration techniques during training. In addition, we compared the performance of the optimized model with DenseNet-121 and GoogLeNet. The results demonstrate a notable enhancement in crack detection accuracy for japonica, reaching 96%, which is a 3.67% improvement over the original model. Furthermore, we achieved a substantial reduction in average training time, reduced by 58.66%. For indica rice, after model optimization and migration, the accuracy reached 96.67%. It’s important to note that the optimized model has limitations and is not suitable for mixed datasets with limited training data. This technology offers the capability to accurately identify and detect cracks in brown rice kernels under visible light conditions, presenting a promising solution for enhancing grain quality during processing.

Devices using emerging technologies and materials with the potential to outperform their silicon counterpart are actively explored in search of ways to extend Moore’s law. Among these technologies, low dimensional channel materials (LDMs) devices, such as carbon nanotube field-effect transistors (CNFETs), are promising to eventually outperform silicon CMOS. As these technologies are in their early development stages, their devices still suffer from high levels of defects and variations, thus unsuitable for nowadays general-purpose applications. On the other hand, applications with inherent error resilience and high-performance demands would suppress the impact of process imperfection and benefit from the performance boost. These applications, including image processing and machine learning through neural networks, would be the ideal targets for adopting these new emerging technologies even in their early stage of technology and process development. In this article, the effects of stuck-at faults in CNFET static random access memory (SRAM)-based multilayer perceptron (MLP) neural network are investigated. The impacts of various fault patterns are analyzed. Several fault recovery techniques are introduced, and their effectiveness is analyzed under different scenarios. With the proposed recovery techniques, the system can recover and tolerate a high level of stuck-at faults up to 40%, paving the path to adopt the early-stage and faulty emerging devices technologies in such high-demand applications.

Agricultural production is a cornerstone of national economies, and the prevalence of plant diseases poses a significant threat to crop yields. Timely disease detection is essential to mitigate these risks. However, manual plant observation methods are labor-intensive and time-consuming, necessitating a shift toward automated solutions. This study addresses the pressing problem of plant disease identification by leveraging advanced image processing techniques. This research begins with a comprehensive analysis of the pepper bell leaf disease dataset. Through a series of meticulously designed image processing steps, the dataset is normalized, enhancing its quality and consistency. Building upon this preprocessing, the UNET segmentation technique in conjunction with the InceptionV3 transfer learning model is employed. This novel approach yields exceptional results, with 99.48% accuracy, 99.97% precision, 99.99% recall, and 99.98% F1 scores. To objectively assess the significance of the proposed model, the performance is benchmarked against existing state-of-the-art models. The findings demonstrate the superiority of the proposed approach in the domain of plant disease identification. By automating the detection process, this research not only enhances efficiency but also enables early disease detection, thereby potentially contributing to the agricultural sector to identify crop disease and manage it efficiently.

The mobility of tourists plays a significant role in shaping their travel experiences and the overall dynamics of a destination. In recent years, the proliferation of social media platforms has provided a rich source of visual data, allowing us to leverage the abundance of pictures shared by tourists to extract meaningful information. Using computer vision techniques and deep learning algorithms, such as object detection, it becomes possible to extract useful information from tourist pictures. In this study, we look for the best way to detect objects from pictures shared by tourists during their journey in order to determine their locations. To achieve our goal we propose a new methodology composed by; database creation, database annotation, preprocessing, deep learning implementation and evaluation. We implemented two deep learning object detection methods: YOLOv7 and Faster R-CNN. A dataset has been created to provide examples of training and testing for neuronal networks. The training was performed on various basic models, in order to increase the efficiency of the training time and to compare the results. We evaluated the results using three parameters: precision, recall and mAP. The results indicate that YOLOv7 has the precision and performance, with over 90 % mAP, 92.1 % precision and 92.7 % recall.

Endoscopic image analysis has played a pivotal function in the diagnosis and management of gastrointestinal (GI) tract diseases. Gastrointestinal endoscopy is a medical procedure where a flexible tube with an endoscope (camera) is inserted into the GI tract to visualize the inner lining of the colon, esophagus, stomach, and small intestine. The videos and images attained during endoscopy provide valuable data for detecting and monitoring a large number of GI diseases. Computer-assisted automated diagnosis technique helps to achieve accurate diagnoses and provide the patient the relevant medical care. Machine learning (ML) and deep learning (DL) methods have been exploited to endoscopic images for classifying diseases and providing diagnostic support. Convolutional Neural Networks (CNN) and other DL algorithms can learn to discriminate between various kinds of GI lesions based on visual properties. This study presents an Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis using an inspired Algorithm with Deep Learning (EIAGTD-NIADL) technique. The EIAGTD-NIADL technique intends to examine the endoscopic images using nature nature-inspired algorithm with a DL model for gastrointestinal tract disease detection and classification. To pre-process the input endoscopic images, the EIAGTD-NIADL technique uses a bilateral filtering (BF) approach. For feature extraction, the EIAGTD-NIADL technique applies an improved ShuffleNet model. To improve the efficacy of the improved ShuffleNet model, the EIAGTD-NIADL technique uses an improved spotted hyena optimizer (ISHO) algorithm. Finally, the classification process is performed by the use of the stacked long short-term memory (SLSTM) method. The experimental outcomes of the EIAGTD-NIADL system can be confirmed on benchmark medical image datasets. The obtained outcomes demonstrate the promising results of the EIAGTD-NIADL approach over other models.

Breast cancer constitutes a significant global health concern that impacts millions of women across the world. The diagnosis of breast cancer involves categorizing grades based on the histopathological characteristics of tumor cells. While histopathological assessment remains the established benchmark for breast cancer diagnosis, it is hampered by time-consuming procedures, subjectivity, and susceptibility to human errors. This study introduces a novel approach called ImageNet-VGG16 (IVNet) for the real-time diagnosis of breast cancer within a hospital environment. The research experiments are conducted using a benchmark dataset known as Jimma University Medical Center (JUMC) breast cancer grading. Advanced image processing techniques are applied to preprocess the data, enhancing performance. This preprocessing involves the utilization of Holistically Nested Edge Detection (HED) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for transformation and stain normalization. We employ advanced neural network-based transfer learning techniques to analyze the preprocessed histopathological images and identify affected cells. Various pre-trained models are utilized, including convolutional neural networks (CNN) such as VGG16, ResNet50, InceptionNetv3, ImageNet, MobileNetv3, and EfficientNetV3, in a comparative framework. The principal objective of this research is the accurate classification of breast cancer images into Grade-1, Grade-2 and Grade-3. Through extensive experimental research, we achieved a commendable 97% correct classification rate by utilizing a hybrid of VGG16 and ImageNet as the proposed feature engineering method, IVNet. We also validate our proposed approach performance using other state-of-the-art study data and statistical t-test analysis. Furthermore, we develop a user-friendly Graphical User Interface (GUI) that facilitates real-time cell tracking in histopathological images. Our real-time diagnosing application offers valuable insights for treatment planning and assists medical professionals in making prognoses. Moreover, our approach can serve as a reliable decision support system for pathologists and clinicians, particularly in settings constrained by limited resources and restricted access to expertise and equipment.

Graphical representations such as chart images are integral to web pages and documents. Automating data extraction from charts is possible by reverse-engineering the visualization pipeline. This study proposes a framework that automates data extraction from bar charts and integrates it with question-answering. The framework employs an object detector to recognize visual cues in the image, followed by text recognition. Mask-RCNN for plot element detection achieves a mean average precision of 95.04% at a threshold of 0.5 which decreases as the Intersection over Union (IoU) threshold increases. A contour approximation-based approach is proposed for extracting the bar coordinates, even at a higher IoU of 0.9. The textual and visual cues are associated with the legend text and preview, and the chart data is finally extracted in tabular format. We introduce an extension to the TAPAS model, called TAPAS++, by incorporating new operations and table question answering is done using TAPAS++ model. The chart summary or description is also produced in an audio format. In the future, this approach could be expanded to enable interactive question answering on charts by accepting audio inquiries from individuals with visual impairments and do more complex reasoning using Large Language Models.

Automated rice variety detection and classification is a task that includes automatically categorizing and identifying varieties or different types of rice based on different characteristics namely grain texture, shape, color, and size. This process is essential for quality assessment, agricultural management, and research purposes. Deep learning (DL) is a subfield of machine leaching (ML) that focuses on training an artificial neural network (ANN) with multiple layers to learn hierarchical representations of data. Convolutional Neural Network (CNN) was widely applied in image-based tasks such as rice variety detection, as they could efficiently capture visual features and patterns. In this study, we propose an Automated Rice Variety Detection and Classification using Quantum Inspired Moth Flame Optimizer with Deep Learning (ARVDC-QIMFODL) technique. The presented ARVDC-QIMFODL technique focuses on the automated identification and classification of distinct kinds of rice varieties. To accomplish this, the ARVDC-QIMFODL technique uses the Median modified wiener filter (MMWF) technique for the noise removal process. Followed by, the feature extraction process takes place by an improved ShuffleNet model. For rice variety detection and classification, the long short-term memory (LSTM) approach was applied. At last, the QIMFO algorithm-based hyperparameter selection process is performed to optimize the detection results of the LSTM system. The simulation outcome of the ARVDC-QIMFODL method is tested on a rice image dataset. An extensive set of experiments showed the remarkable efficiency of the ARVDC-QIMFODL system over other models.

The surface cracks on high-speed railway ballastless track slabs directly influence their lifespan, while the efficiency of damage detection and maintenance is crucial for ensuring operational safety. Leveraging deep learning image processing technology can significantly enhance detection efficiency. Therefore, in response to the specific attributes of ballastless track slab crack detection, this paper introduces the RSG-YOLO model. By implementing a reparameterized dual-fused feature pyramid structure, we bolster the network’s feature extraction capacity and curtail the loss of crack features during extraction. SIoU is used to replace CIoU to optimize the bounding box regression loss function, reduce the degrees of freedom of the loss function, and improve the convergence speed The GAM attention mechanism is integrated to heighten the model’s responsiveness to diverse channel information. The proposed RSG-YOLO model was evaluated against mainstream models in the field of crack detection. The results demonstrated improved detection accuracy and recall rates. Specifically, when compared to baseline models, our approach exhibited significant advancements in reducing both missed detections and false alarms. These improvements were quantified by a 4.34% increase in crack detection accuracy and a 3.08% rise in mAP_0.5. Consequently, the RSG-YOLO model effectively enables precise detection of track slab cracks.

We present a novel framework for simulating time-varying decay effects for natural images. Conventional methods assume the input image includes enough decay information and uses the color or texture information of the decayed regions to transfer its effect to the non-decayed regions. Unlike these approaches, our framework generates diverse patterns of decay effects by leveraging a decay effects generator network without referencing the decay features of the input image, which allows us to handle more general images with non-decayed objects. Our decay generator network is formed by a style-based generative adversarial network with an arbitrary-sized stationary texture generation mechanism that allows us to synthesize various sizes of decay textures. This arbitrary-sized stationary texture generation is necessary to synthesize photo-realistic decay effects since the appropriate resolutions of the decay textures depend on those of the target objects. We construct a novel decay texture image dataset that contains various types of decay texture images, such as mossy and rust, to train the decay generator network. We show that our framework is able to synthesize diverse decay effects on various non-decayed objects without using additional decayed object images.

The growth of irregular brain cells leads to a disease called brain tumor (BT). It is difficult to predict a patient’s chance of survival due to the low rate and wide range of tumor shapes. Even though it is possible to manually detect cancer, doing so is difficult and time-consuming and runs the risk of producing false-positive results. This can be done via MRI, which is necessary for locating cancer. It is very difficult to reliably identify different illnesses from MRI images for successful therapy via a computer-aided diagnostic system. In the experiment, three openly accessible benchmark datasets were utilized. To perform feature extraction in our proposed method, a CNN model was employed, followed by the application of five machine learning classifiers: Decision tree (DT), Naive Bayes (NB), Adaptive Boosting (AdaBoost), K-nearest neighbor (KNN), and support vector machine (SVM). The outcomes show that the proposed CNN architecture with the KNN classifier performs better than previous CNN models by outperforming other cutting-edge DL models under various classification metrics. Finally, the achieved F1-Score, precision, recall, and accuracy values for the classification and detection of the proposed model were 99.58%, 99.59%, 99.58%, and 99.58%, respectively. For the comparison study, additional Transfer Learning models are utilized. Experimental findings support the strength of the proposed architecture, which has rapidly accelerated and improved the classifications of BTs. The designed method outperforms the body of existing knowledge, demonstrating that it is a quick and precise method for classifying BTs.

This work introduces new effective methodologies for the detection, analysis, and classification of diverse defects that may occur throughout the production process of photovoltaic panels. In this context, this work proposes a novel approach that combines Image Processing and Vision Transformers (ViT) to address this challenge. The results of this work comprise a light flaw-type classifier based on ViT, along with computational tools to calculate the length of cracks and the proportional damaged area caused by flaws without requiring the training of other models. The proposed ViT-μ model achieved high accuracy in flaw detection and classification for solar cells, with rates of nearly 98% and 94%, respectively; achieved with a mere one-hour training duration. Moreover, this study introduces a weakly supervised method of visualizing the detected defects within a solar cell, by using attention maps.

Convolutional neural network (CNN) is quite popular in computer vision, especially in image classification with excellent performance. However, limited by the convolution kernels, CNN-based classifiers are hard to extract global feature from the original image, while exact object locations in the environment are included in the global feature. One popular way to improve global feature extraction performance is to use graph neural network (GNN) which can aggregate global information through the connection relationship of different nodes. In this work, a novel end-to-end graph neural network architecture is proposed, in which local and global-attention feature are used simultaneously to achieve more accurate predictions. In this architecture, a CNN block is designed to learn local feature and graph convolutional neural network (GCN) is used to learn global feature. Global-attention feature for final prediction is down-sampled from global feature by the proposed global multi-head self-attention pooling (GMSAPool) based on self-attention mechanism, which reconstructs the input graph by introducing virtual node and automatically assigns different weights to each node to obtain a more representative global-attention feature. In addition, the proposed architecture can be trained without converting images to graphs in advance, and the computational burden can also be reduced. This approach is demonstrated on three open datasets (Agricultural Disease, Caltech256 and CIFAR-100) to validate the effectiveness. The determined experimental results showed that: 1) The proposed model achieve 84.46%, 77.80%, and 83.33% on the Macro-F1 in three datasets respectively, improving over the best baselines; 2) Global-attention feature that is more conducive to the final prediction is extracted by GMSAPool from numerous nodes, in which Macro-P,Macro-R and Macro-F1 are respectively improved 3.655%,1.12%,2.715% on average.

One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize medicine by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven healthcare a very lucrative area, in terms of data inference and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. The main focus is developing new methods for segmenting histopathological images of prostate gland using a multi-channel algorithm with an attention mechanism to detect important areas. We compare our results with a host of contemporary techniques and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model we propose for semantic segmentation of prostate glands the potential to provide accurate segmentation versus tumor features, which has significant implications for medical screening applications.

The current algorithm of encrypting images uses low-dimensional chaotic systems with a limited key space and poor security, while high-dimensional chaotic systems are difficult to implement and inefficient. Additionally, if image encryption algorithms use fixed DNA encoding rules, they could be cracked; dynamic coding can easily deviate from optimal solutions, thus causing algorithm instability. Based on this, a novel technique for image encryption is presented in this article. It is possible to avoid linear correlation of the chaotic sequence by randomly jumping on two uncorrelated one-dimensional chaoses. Meanwhile, numerous encryption results are generated in parallel by randomly grouping DNA encoding groups and encoding operations, and the optimal solution is then selected based on the encryption results, thereby minimizing ciphertext image instability to some extent. According to the experimental results, this algorithm produces high-security, plaintext-sensitive, and non-cracking encrypted images. Furthermore, this algorithm produces excellent encryption results both on color and sized images, and is applicable to a wide variety of applications.

Underwater images typically exhibit color bias and low contrast resulting from the absorption and scattering effects of light propagating in water. Such degraded images often have difficulty in satisfying the requirements of underwater operations. Traditional underwater image restoration and enhancement methods generally require long computation times with unsatisfactory results. In this study, we propose a new underwater image restoration method involving background light estimation based on double-opponency and transmission estimation based on the red channel prior. For background light estimation, double-opponency is adopted to imitate human visual color constancy, whereby background light information consistent with human perception is obtained. Transmission estimation uses the prior knowledge that red wavelength has the fastest attenuation in water. To enhance the contrast effect of the resulting image, the restored image is fused with that of contrast stretch processing in HSV space to obtain the final output image. Experiments indicate that the proposed method can reduce computation time by 40% or more relative to comparison methods, thereby producing a clearer normal color image according to both objective and subjective evaluations

Once an image has been processed by, for example, a robot machine, for the purpose of, for example, features extraction or meaningful information retrieval, has a secure scheme been applied to preserve security and privacy of such information before sending it to another processing party? A huge number of image-related Internet of Things (IoT) applications face such an issue. But what are applied and potentially being applied image processing techniques that have contributed to enhance the security and privacy of IoT applications? There are numerous IoT applications that utilize image processing techniques in this direction. This article aims to survey and review a number of recently published papers and research studies that encompass proposed methods in which image processing techniques are applied to enhance the security, privacy, and safety of IoT applications. It also aims to help interested researchers in related fields have insights on what the role of image processing in enhancing the security of IoT applications is and what those techniques applied to enhance the security of IoT applications are. A comprehensive framework has been graphically extracted to give readers in the field of IoT security a map with the suitable image processing techniques that serve better to enhancing IoT applications in terms of security and privacy.