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This paper focuses on how to reduce cheating and minimize errors while automatically grading paper-based multiple-choice questions (MCQ) by making the whole process relatively fast, less expensive, more credible, and fairer especially when the number of examinees and number of questions are large. Credibility is obtained when techniques and best practices are introduced in the design process of MCQ. Fairness is obtained by personalizing evaluation through permutation of answers and questions. The distance introduced in personalization has led to the modification of the traditional automatic grading process where an application mapping the test number with its responses in the grading software is loaded automatically at each start of the grading process. On the extracted header fields, 2DFFT is applied as well as the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The minimization of image processing errors is then obtained by training a support vector machine (SVM) for handwriting optical character recognition (OCR) using the Mixed National Institute of Standards and Technology (MNIST) dataset with 99.5% accuracy. The tests are carried out in several subjects at Fotso Victor University Institute of Technology (UIT) in Bandjoun and the ColTech of the University of Bamenda and teachers as well as students after investigation have confirmed that our method reduces cheating and improves the error rate during grading with fewer complaints. [ABSTRACT FROM AUTHOR]

Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework's integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy. [ABSTRACT FROM AUTHOR]

Railway transportation has been integrated into people's lives. According to the "Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System" issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution. [ABSTRACT FROM AUTHOR]

Nowadays, more and more brands use interesting anime characters to promote and increase brand awareness. However, real-time and interesting promotional materials are also one of the key factors to attract people, so real-time processing of anime characters has also become an effective way to enhance brand awareness. In recent years, with the rapid development of deep learning, image style conversion based on AI technology has become a much-attended artificial intelligence application, but it also suffers from the disadvantages of complex model structure, slow conversion speed, and inconspicuous identity features, which need to be improved. In view of this, this study proposes an anime portrait realization (ARF-GAN) algorithm based on generative adversarial network. This algorithm is based on the Pix2Pix architecture and also uses the U-net network structure with jump connections to connect the encoder’s feature maps directly to the decoder, which in turn enables the network to reconstruct the output data in a better way and the network architecture is more lightweight.This algorithm is based on the Pix2Pix architecture and also uses the U-net network structure with jump connections to connect the encoder’s feature maps directly to the decoder, which in turn enables the network to reconstruct the output data in a better way and the network architecture is more lightweight. It also introduces the CBAM module, which can enhance the model’s expressive and generative capabilities without increasing the model’s complexity, and improve the model’s real-time image processing capability. In addition, this paper also compensates for the problem of image blurring brought by the original architecture by introducing the deblurring module. The experimental results on CartoonFaceAB, DanBooru show that the proposed ARF-GAN has a better generative effect for the task of realizing anime portraits. And comparing with different types of image generating models, it shows better accuracy and lower complexity by using several evaluation indexes such as PSNR and SSIM. This makes it more suitable for brand advertisement promotion, so it has good application value for improving brand awareness. [ABSTRACT FROM AUTHOR]

Using glass as a structural element that could obtain the role of the primary load-bearing elements of iconoclast structures has had a superficial history. Because of its aesthetic and admissible mechanical properties, glass has been considered one of the structural elements in the construction industry. Because of its extrasafety, laminated glass on top of the most widely used elements has been appraised with more depth. The study aimed to evaluate the failure modes in laminated glass and the possibility of detecting the pre-failure status of the surveyed elements, referring to the proffered approaches and image processing (IP) procedures in the literature. The outcomes have compared the peculiarity and strength points of various routes and have reached the inference for proper approaches in every procedure. According to the results, image processing (IP) is the most appropriate method of pre-failure detection in load-bearing laminated glass elements. Among the image processing (IP) techniques, digital image correlation has the widest application. The results of this study, in combination with other pieces of research, may propound a database for evaluating the failure status of glass structures. [ABSTRACT FROM AUTHOR]

The recovery of mutilated fingerprints plays an important role in improving the accuracy of fingerprint recognition and the speed of identity retrieval, so it is crucial to recover mutilated fingerprints efficiently and accurately. In this paper, we propose a method for the restoration of mutilated fingerprints based on the ridge texture and orientation field. First, the part to be restored is identified via the local quality of the fingerprint, and a mask image is generated. Second, a novel dual-stream fingerprint restoration network named IFSR is designed, which contains two branches, namely an orientation prediction branch guided by the fingerprint orientation field and a detail restoration branch guided by the high-quality fingerprint texture image, through which the damaged region of the mutilated fingerprint is restored. Finally, the method proposed in this paper is validated on a real dataset and an artificially damaged fingerprint dataset. The equal error rate (EER) achieved on the DB1, DB2, and DB4 datasets of FVC2002 is 0.10%, 0.12%, and 0.20%, respectively, while on the DB1, DB2, and DB4 datasets of FVC2004, the EER reaches 1.13%, 2.00%, and 0.27%, respectively. On the artificially corrupted fingerprint dataset, the restoration method achieves a peak signal-to-noise ratio (PSNR) of 16.6735. [ABSTRACT FROM AUTHOR]

The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist. [ABSTRACT FROM AUTHOR]

Currently, deep learning methods for low‐light image enhancement tasks mainly focus on the illumination of images, while neglecting the problems of image noise and feature loss. To address this issue, this paper proposes a novel low‐light image enhancement network called DAF‐Retinex, based on the Retinex‐Net. To address the issue of image noise, different from traditional image denoise methods, this paper utilizes a fully convolutional neural network to denoise the reflection component, additionally, a denoising loss function is introduced to suppress noise. For preserving image details and extracting features, this paper creatively introduces self‐calibrated convolutions into low‐light image enhancement tasks, furthermore, a feature augmented attention block consisting of feature‐guided attention (FGA) is designed for feature learning to effectively enhance image illumination and extract image detail features. Experimental results demonstrate that the proposed algorithm in this paper effectively removes image noise and extracts detailed features, resulting in visually improved outcomes. On public datasets, the average improvement in objective evaluation metrics of image quality such as PSNR, SSIM, and NIQE are 1.13%, 4.12%, and 1.28%, respectively. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

To communicate with one another hand, gesture is very important. The task of using the hand gesture in technology is influenced by a very common way humans communicate with the natural environment. The recognizing and finding pose estimation of hand comes under the area of hand gesture analysis. To find out the gesturing hand is very difficult than finding the another part of the human body because the hand is smaller in size. The hand has greater complexity and more challenges due to differences between the cultural or individual factors of users and gestures invented from ad hoc. The complication and divergences of finding hand gestures will deeply affect the recognition rate and accuracy. This paper emphasizes on summary of hand gestures technique, recognition methods, merits and demerits, various applications, available data sets, and achieved accuracy rate, classifiers, algorithm, and gesture types. This paper also scrutinizes the performance of traditional and deep learning methods on dynamic hand gesture recognition. [ABSTRACT FROM AUTHOR]

In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or a stroke. Given the lack of diagnostic utility of instrumental methods in acute isolated vertigo, the differentiation of vestibular neuritis and stroke is primarily clinical. As a part of the initial differential diagnosis, the physician focuses on the characteristics of nystagmus and the results of the video head impulse test (vHIT). Instruments for accurate vHIT are costly and are often utilized exclusively in healthcare settings. The objective of this paper is to review contemporary methodologies for accurately detecting the position of pupil centers in both eyes of a patient and for precisely extracting their coordinates. Additionally, the paper describes methods for accurately determining the head rotation angle under diverse imaging and lighting conditions. Furthermore, the suitability of these methods for vHIT is being evaluated. We assume the maximum allowable error is 0.005 radians per frame to detect pupils' coordinates or 0.3 degrees per frame while detecting the head position. We found that for such conditions, the most suitable approaches for head posture detection are deep learning (including LSTM networks), search by template matching, linear regression of EMG sensor data, and optical fiber sensor usage. The most relevant approaches for pupil localization for our medical tasks are deep learning, geometric transformations, decision trees, and RASNAC. This study might assist in the identification of a number of approaches that can be employed in the future to construct a high-accuracy system for vHIT based on a smartphone or a home computer, with subsequent signal processing and initial diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, on the basis of in-depth research on the key technology of binocular vision measurement; a set of multidimension online measurement system for image recognition is built. Canny operator is used as a tool to detect the contour features of parts, and the Canny operator is accelerated and improved from the aspects of mathematical reasoning and Gaussian pyramid. A synchronous external trigger circuit for a binocular camera and light source was designed. Finally, the improved algorithms in various aspects of visual measurement in this paper are applied to the measurement system. The experimental results show that the online measurement system has the advantages of high measurement accuracy and small repeatability errors. [ABSTRACT FROM AUTHOR]

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity. Antenna defects, ranging from manufacturing imperfections to environmental wear, pose significant challenges to the reliability and performance of communication systems. This review paper navigates the landscape of antenna defect detection, emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection. This review paper serves as a valuable resource for researchers, engineers, and practitioners engaged in the design and maintenance of communication systems. The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures. In this study, a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented. The PRISMA principles will be followed throughout the review, and its goals are to provide a summary of recent research, identify relevant computer vision techniques, and evaluate how effective these techniques are in discovering defects during inspections. It contains articles from scholarly journals as well as papers presented at conferences up until June 2023. This research utilized search phrases that were relevant, and papers were chosen based on whether or not they met certain inclusion and exclusion criteria. In this study, several different computer vision approaches, such as feature extraction and defect classification, are broken down and analyzed. Additionally, their applicability and performance are discussed. The review highlights the significance of utilizing a wide variety of datasets and measurement criteria. The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation, such as real-time inspection systems and multispectral imaging. This review, on its whole, offers a complete study of computer vision approaches for quality control in antenna parts. It does so by providing helpful insights and drawing attention to areas that require additional exploration. [ABSTRACT FROM AUTHOR]

In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and L r and L 0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D , r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution. [ABSTRACT FROM AUTHOR]

This paper proposes an automated inspection approach for printed circuit boards (PCBs) that can accurately locate defects to solve the issues of low precision, complex equipment, and high cost. Digital image processing techniques are utilized in this method, including filtering, image segmentation, feature extraction, alignment, and mathematical morphology processing. To overcome the Otsu thresholding segmentation algorithm's high computational cost and poor real-time performance, a particle swarm approach is optimized to increase image segmentation efficiency. Meanwhile, combining the benefits of the FLANN algorithm and the SURF method, matching image feature points is done based on the SURF algorithm. The performance of matching image feature points is improved. In addition, the alignment error of the images is reduced. According to experimental results, the improved PCB defect detection algorithm demonstrated 98.9% accuracy, with remarkable efficiency and accuracy, and can satisfy PCB defect detection requirements. [ABSTRACT FROM AUTHOR]

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

Comic inpainting is essential for language localization and adapting comics into animations, involving the removal of speech boxes, textboxes, and sound effects while maintaining artistic integrity. This paper introduces an automated approach leveraging the Manga inpainting method by Xie et al., adapted for western comics like Tex Willer. Tex Willer comics, known for their fusion of Italian art and American Western imagery, pose unique challenges due to their distinct styles. Customized Manga Restoration and Structural Line Extraction codes are employed, along with automated mask generation using deep CNN-based speech balloon detection and segmentation. Our contributions include the development of a precise model tailored to Tex Willer Comics, resulting in visually coherent inpainting results. The paper follows a structured format, covering State-of-the-Art, methodology, results, and conclusions, aiming to streamline the process of western comic inpainting for professionals in the comics and animation industries. [ABSTRACT FROM AUTHOR]

The discrepancy in data distribution between training and testing scenarios, as well as the inductive bias of convolutional neural networks towards image styles, reduces the model's generalization ability. Many unsupervised domain generalization methods based on feature decoupling suffer from an initial neglect of explicit decoupling of content and style features, resulting in content features that still contain considerable redundant information, thereby restricting improvements in generalization capability. To tackle this problem, this paper optimizes the learning process of domain‐invariant (content) features into an information compression issue, minimizing redundancy in content features. Furthermore, to enhance decoupled learning, this paper introduces innovative cross‐domain loss functions and image reconstruction modules that explicitly decouple and merge content and style across different domains. Extensive experiments demonstrate the method's significant enhancements over recent cutting‐edge approaches. [ABSTRACT FROM AUTHOR]

Current research on apron conflict detection is often limited to the interaction between the aircraft as a whole and other objects, making it difficult to accomplish targeted identification of vulnerable and high‐cost aircraft components. However, the implementation of detailed aircraft identification is of great significance to enhance the safety of airport surface operations. Based on the excellent performance of High‐Resolution Network (HRNet) in keypoint detection, a lightweight end‐to‐end keypoint detection network, namely Squeeze and Excitation High‐Resolution Network (SEHRNet), is proposed in this paper to solve the problems of HRNet's slower computation and more redundancy. First, the errors arising from coordinate transformations in the coding and decoding process are solved by an improved feature map coding and decoding process. Second, replace the BasicBlock in HRNet with the Depthwise separable convolutions based on the Squeeze‐and‐Excitation Networks, which drastically cuts the computational cost of the network. Third, the improved Bottleneck module is used to further enhance the capability of feature extraction. Experimental results prove that, based on the aircraft keypoint detection dataset, the SEHRNet proposed in this paper shows stronger applicability compared to the current mainstream networks. Compared with the original HRNet, the improved network has higher accuracy, faster speed, and lighter model for aircraft keypoint detection. [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]

Using the improved Johnson et al.'s style migration network as a starting point, this paper proposes a new loss function based on the position information Gram matrix. The new method adds the chunked Gram matrix with position information, and simultaneously, the structural similarity between the style map and the resultant image is added to the style training. The style position information is given to the resultant image, and finally, the resolution of the resultant image is improved with the SRGAN. The new model can effectively migrate the texture structure as well as the color space of the style image, while the data of the content image are kept intact. The simulation results reveal that the image processing results of the new model improve those of the classical Johnson et al.'s method, Google Brain team method, and CCPL method, and the SSIM values of the resulting map and style image are all greater than 0.3. As a comparison, the SSIM values of Johnson et al., Google Brain team, and CCPL are 0.14, 0.11, and 0.12, respectively, which is an obvious improvement. Moreover, with deeper training, the new method can improve the similarity of certain resulting images and style images to more than 0.37256. In addition, training other arbitrary content images on the basis of the trained model can quickly yield satisfactory results. [ABSTRACT FROM AUTHOR]

The rapid advancements in deep learning techniques have significantly impacted various fields, including mathematics and natural language processing. Mathematical equation identification and solving is a challenging task that has been studied by researchers for many years. Traditional approaches to this task typically rely on rule-based systems, hand-crafted features or manual work. However, these approaches are often limited in their ability to handle complex equations and there are also chances of error in it. The survey begins by discussing the challenges associated with mathematical equation identification and solving, such as the complexity of mathematical notation, variable representation, and equation structure. The paper provides an overview of the existing methodologies, algorithms, and frameworks employed in the development of systems capable of identifying and solving mathematical equations using deep learning techniques. [ABSTRACT FROM AUTHOR]

Fabric pilling can significantly affect the usability and appearance of fabrics; making the evaluation of pilling grades a crucial aspect of textile quality inspection. Currently, manual visual inspection is the mainstream strategy in the industry for assessing pilling, but this method suffers from low efficiency and inconsistent accuracy. Therefore, there is a need for a more precise and efficient method to evaluate textile pilling grades. In this paper, we propose an objective system for assessing fabric pilling grades using a deep learning model in conjunction with a gradient boosting decision tree classification algorithm. The system comprises a multi-light source fabric image acquisition setup, an Attention-enhanced UNet model, and a gradient boosting decision tree classifier. The process begins with capturing fabric images, which are then segmented using the Attention-enhanced UNet model. This model effectively extracts pilling features, including the number of pills, the pilling area, and the pilling coverage ratio. Our experimental results show that this segmentation method outperforms traditional filtering and edge detection algorithms in terms of efficiency. The attention mechanism integrated into the UNet model significantly enhances the segmentation performance, particularly for fabric pilling. By combining the extracted features as input for the fabric evaluation algorithm, our proposed method achieves an impressive classification accuracy of 98.47% in objectively evaluating fabric pilling grades. This approach offers a substantial improvement in both the accuracy and efficiency of fabric pilling grade assessment. [ABSTRACT FROM AUTHOR]

Flame temperature field measurement has always been a key topic in combustion research, which is of great significance for combustion state diagnosis and fuel combustion optimization. Deep learning technology exploits its superior nonlinear ability to rapidly reconstruct the three-dimensional (3D) temperature field of flames from flame light-field images. However, existing algorithms have problems with complex networks, poor noise resistance and low reconstruction accuracy. This paper proposes a lightweight 3D flame temperature field reconstruction algorithm based on an improved MobileNet that combines residuals and spatial attention mechanisms. This method basically achieves a balance between low complexity and high accuracy and has good noise resistance performance. Simulation results show that the average relative error of temperature field reconstruction on the noise-free unimodal flame dataset is only 0.022%, and its computational complexity is only one-tenth of the existing CNN. The noise simulation experiment shows that this method has good noise resistance performance. The maximum relative error and the average relative error at Gaussian white noise standard deviation σ = 0.15 are only 2.91% and 0.27%, respectively. [ABSTRACT FROM AUTHOR]

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. [ABSTRACT FROM AUTHOR]

Fires not only cause devastating consequences for human life and property, but also lead to soil erosion in forests. Therefore, it is necessary to design a novel algorithm that can quickly monitor smoke from fires. Most existing smoke segmentation methods do not consider the segmentation accuracy of algorithms under limited computational resources. To address this research gap, this paper proposes a lightweight smoke segmentation algorithm based on DeepLabV3+ that achieves fast inference speed and high accuracy for different sizes smoke. To reduce the number of parameters, the feature extraction network of the DeeplabV3+ algorithm is replaced by MobileNetV2, which enhances the extraction ability of the algorithm in segment smoke images. Then, the Convolutional Block Attention Module (CBAM) is added to the encoder part to enhance the perception of the algorithm for small smoke and effectively alleviates smoke mis‐segmentation. Furthermore, a newly designed loss function is used in the network. The experimental results show that the proposed method has improved by 1.27% in Smoke IoU and 1.21% in mPA compared with other methods. The weight size has been reduced to 10.76% of the original DeepLabV3+, and the inference time is only 33.71ms. Therefore, it is a more suitable early fire detection algorithm for resource‐constrained environments. [ABSTRACT FROM AUTHOR]

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area. [ABSTRACT FROM AUTHOR]

During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer preparation and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be arranged in various configurations from 1 × 5 to 5 × 7, and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing. [ABSTRACT FROM AUTHOR]

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis. [ABSTRACT FROM AUTHOR]

Globally, lung cancer is the primary cause of cancer-related mortality. Higher chance of survival depends on the early diagnosis of lung nodules. Manual lung cancer screenings depends on the human factor. The variability in size, texture, and shape of lung nodules may pose a challenge for developing accurate automatic detection systems. This article proposes an ensemble approach to tackle the challenge of lung nodule detection. The goal was to improve prediction accuracy by exploring the performance of multiple transfer learning models instead of relying solely on deep learning models. An extensive dataset of CT scans was gathered to train the built deep learning models. This research paper is focused on the Convolutional Neural Networks' (CNNs') ability to automatically learn and adapt to discernible features in the lung images which is particularly beneficial for accurate classification, aiding in identifying true and false labels, and ultimately enhancing lung cancer diagnostic accuracy. This paper provides a comparative analysis of the performance of CNN, VGG-16, and VGG-19. Notably, the built transfer learning model VGG-16 achieved a remarkable accuracy of 95%, surpassing the baseline method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient positioning accuracy of the ring spinning spun yarn automatic jointing machine using an automated guided vehicle platform after spun yarn breakage, a three-dimensional coordinate detection method of the ring spinning spun yarn spindle position based on binocular vision is proposed. The bottom column of the spindle was selected as the detection target, the contrast of the background of the bottom column of the spindle was physically enhanced, the picture of the bottom of the spindle was collected with a calibrated binocular camera, the two-dimensional coordinates of the midpoint of the bottom edge of the spindle bottom profile were obtained by the processing of the local picture, and the three-dimensional world coordinates of the midpoint of the bottom edge of the spindle bottom profile were calculated based on the method of single-pair feature-point matching. Comparing this paper's single-pair feature-point matching method with the traditional multi-pair feature-point matching detection method (semi-global block matching), the experimental results showed that: the detection errors of both the single-pair feature-point matching method and the traditional semi-global block matching detection method were less than 2 mm; both methods satisfied the requirements of the positional detection accuracy of the spindle joints. The position detection method based on single-pair feature-point matching saved 80% of the program time compared with the traditional semi-global block matching method. [ABSTRACT FROM AUTHOR]

The quality of images captured digitally or transmitted over networks is distorted by noise during the process. The current methods of image restoration can be ineffective in dealing with intricate noise patterns or may be slow or imprecise. This paper fills this gap by presenting a new second-order continuous-time dynamical system for denoising of images in image restoration. The approach used in this work poses the problem as a convex quadratic program that can, thus, be solved for optimality. The existence and uniqueness of a global solution are theoretically demonstrated, and the condition for the global strong convergence of the system's trajectory is provided. The method presented in this paper is shown to be useful in a number of experiments on image restoration. As for the performance, it is higher than that of other known algorithms, with an average SNR equal to 34.78 and a Structural Similarity Index Measure (SSIM) of 0.959 for the reconstructed images. Such improvements demonstrate the effectiveness of the second-order dynamical system approach in actual image restoration applications. [ABSTRACT FROM AUTHOR]

This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management. [ABSTRACT FROM AUTHOR]

The accurate distribution of joints on the tunnel face is crucial for assessing the stability and safety of surrounding rock during tunnel construction. This paper introduces the Mask R-CNN image segmentation algorithm, a state-of-the-art deep learning model, to achieve efficient and accurate identification and extraction of joints on tunnel face images. First, digital images of tunnel faces were captured and stitched, resulting in 286 complete images suitable for analysis. Then, the joints on the tunnel face were extracted using traditional image processing algorithms, the commonly used U-net image segmentation model, and the Mask R-CNN image segmentation model introduced in this paper to address the lack of recognition accuracy. Finally, the extraction results obtained by the three methods were compared. The comparison results show that the joint extraction method based on the Mask R-CNN image segmentation deep learning model introduced in this paper achieved the best joint extraction effect with a Dice similarity coefficient of 87.48%, outperforming traditional methods and the U-net model, which scored 60.59% and 75.36%, respectively, realizing accurate and efficient acquisition of tunnel face rock joints. These findings suggest that the Mask R-CNN model can be effectively implemented in real-time monitoring systems for tunnel construction projects. [ABSTRACT FROM AUTHOR]

Monitoring and evaluating fish growth and health relies on accurate aquaculture body size and weight data. Pearl's gentian grouper has tender flesh and rapid growth, and the scale of aquaculture is increasing yearly. Moreover, it is inefficient to measure body size and weight data manually regularly, so in this paper, taking pearl gentian grouper as the research object, we propose a body size and weight estimation method based on machine vision and improve DeepLabV3 + algorithm, with the MIoU reaching 97.37% on the self-constructed dataset, to extract the closed contour of the fish body curve, then apply the algorithm for measuring body scale traits. This paper determines the critical points of body scale calculation. It uses the calibration coefficients to calculate the body scale parameters and then estimate fish body weight. In the established body weight estimation model, the coefficient of determination of the CatBoost regression model based on the total length, body height, body thickness, and area of the fish was R2 of 0.987 which realized the high-precision estimation of the body weight of the pearl gentian grouper and provided the basis for intelligent grading. [ABSTRACT FROM AUTHOR]

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles. [ABSTRACT FROM AUTHOR]

The airlift pump is a key part of wastewater treatment and is employed as an innovative and feasible collection tool. However, as one of the key factors in the lifting capability of airlift pumps, film thickness in the gas–liquid two-phase flow operating in pumps is still an unknown topic because it is difficult to measure. This paper proposes a visualization method for measuring film thickness and investigates the film thickness when operating under gas flow with a high rate in airlift pumps using experiments. Firstly, a simulation experiment platform was built, and the images of the film structure were acquired by a high-speed camera. Then, image-processing technology and an image distortion correction were proposed to extract the gas–liquid interface for studying the thickness of the film. The experimental results demonstrated that a large film thickness ranging from 0.15 D to 0.24 D was found in airlift pumps and that its film thickness kept a constant value, even under a high gas superficial velocity, maintaining a large output liquid flow from airlift pumps. As wastewater was carried by wastewater treatment, a larger film thickness of the annular film will benefit the high lifting rate of wastewater. The works in this paper offer valuable insights for the higher performance of working airlift pumps and wastewater treatment efficiency. [ABSTRACT FROM AUTHOR]

The Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries. Plain Language Summary: We generated a global mosaic of Mars using Context Camera satellite imagery at 5.0 m/pixel, a substantial improvement in spatial resolution over the previous global mosaic at 100 m/pixel. This paper describes a new technique for merging overlapping images together in a way that preserves all information from each component image and produces a map of all image boundaries that is included with the mosaic. This makes scientific analyses that are facilitated by the mosaic fully traceable and reproducible. With this mosaic, scientists can now readily analyze landforms without the overhead of downloading individual images and perform complete global analyses of Mars at a resolution capable of seeing rock outcrops on the surface. Key Points: A global mosaic of Mars at 5.0 m/pixel has been created from 86,571 Context Camera images that are co‐registered to each other and seam‐correctedNon‐destructive image processing was used to preserve all image metadata and map seamsThe new mosaic will facilitate systematic studies of Mars at 5.0 m/pixel spatial resolution [ABSTRACT FROM AUTHOR]

With the rapid advancement of modern hardware technology, breakthroughs have been made in many areas of artificial intelligence research, leading to the direction of machine replacement or assistance in various fields. However, most artificial intelligence or deep learning techniques require large amounts of training data and are typically applicable to a single task objective. Acquiring such large training datasets can be particularly challenging, especially in domains like medical imaging. In the field of image processing, few-shot image segmentation is an area of active research. Recent studies have employed deep learning and meta-learning approaches to enable models to segment objects in images with only a small amount of training data, allowing them to quickly adapt to new task objectives. This paper proposes a network architecture for meta-learning few-shot image segmentation, utilizing a meta-learning classification weight transfer network to generate masks for few-shot image segmentation. The architecture leverages pre-trained classification weight transfers to generate informative prior masks and employs pre-trained feature extraction architecture for feature extraction of query and support images. Furthermore, it utilizes a Feature Enrichment Module to adaptively propagate information from finer features to coarser features in a top-down manner for query image feature extraction. Finally, a classification module is employed for query image segmentation prediction. Experimental results demonstrate that compared to the baseline using the mean Intersection over Union (mIOU) as the evaluation metric, the accuracy increases by 1.7% in the one-shot experiment and by 2.6% in the five-shot experiment. Thus, compared to the baseline, the proposed architecture with meta-learning classification weight transfer network for mask generation exhibits superior performance in few-shot image segmentation. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial role in fostering sustainable growth through the integration of various technological advancements such as image processing, artificial intelligence, deep learning, and the Internet of Things (IoT). The global population is increasing on a daily basis. The increasing demand within the agriculture industry has necessitated the collective enhancement of plant cultivation and field productivity. This paper emphasizes the significance of effectively managing the crop during its initial growth phase as well as during the harvesting era. Image processing and artificial neural networks are employed as distinct methodologies for detecting illnesses on leaves. When capturing images using drones, the resulting images undergo a process of segmentation and transformation, resulting in the identification of three distinct vectors that represent diseases. These vectors include colour, texture, and morphology. This paper reviews on various disease classification strategies that can be utilized for the detection of plant diseases. [ABSTRACT FROM AUTHOR]

The matching problem often arises in image processing and involves finding a correspondence between similar objects. In particular, variational matching models optimize suitable energies that evaluate the dissimilarity between the current shape and the relative template. A penalty term often appears in the energy to constrain the regularity of the solution. To perform numerical computation, a discrete version of the energy is defined. Then, the question of consistency between the continuous and discrete solutions arises. This paper proves a Γ-convergence result for the discrete energy to the continuous one. In particular, we highlight some geometric properties that must be guaranteed in the discretization process to ensure the convergence of minimizers. We prove the result in the framework introduced in the 2017 paper of Charlier et al., which studies the matching problem between geometric structures carrying on a signal (fshapes). The matching energy is defined for L2 signals and evaluates the difference between fshapes in terms of the varifold norm. This paper maintains a dual attachment term, but we consider a BV penalty term in place of the original L2 norm. [ABSTRACT FROM AUTHOR]

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to perform image segmentation. CNNs are a type of deep learning model that is well-suited for image processing tasks. The CNN in this paper was trained on a local city dataset, and it was able to achieve a mean intersection over union (IoU) of 73%. IoU is a measure of how well the segmentation results match the ground truth labels. A score of 100% indicates that the segmentation is perfect, while a score of 0% indicates that the segmentation is completely wrong. This means that the method can segment images at a very fast rate, which is important for autonomous vehicles that need to make real-time decisions. Overall, the proposed method is a promising approach for image segmentation in autonomous vehicles. It can achieve high accuracy and speed, and it is easy to implement using Python. The proposed method attains an accuracy of 98.34 %, a Sensitivity of 97.26 % and a sensitivity of 96.37 % had been attained. The method could be used to improve the safety and efficiency of autonomous vehicles by enabling them to better understand their surroundings. [ABSTRACT FROM AUTHOR]

This paper presents a textile defect detection method that utilizes a multi‐proportion spatial attention mechanism and channel memory feature fusion network by addressing the difficulties presented by complicated shapes and large size variations. In particular, a multi‐proportion spatial attention mechanism (MPAM) is introduced, which employs multi‐proportion convolution to improve the backbone network's capacity to detect non‐uniform structural defects. Additionally, the generality and adaptability of the model are enhanced by a multi‐scale spatial pyramid pooling structure (MS‐SPP). Second, a channel attention mechanism‐based memory feature fusion network is developed, which incorporates channel attention to adaptive weight the feature channels, focusing on crucial information channels to efficiently fuse contextual features and enhance the model's memory capacity. Finally, a novel efficient Wise‐IoU (EWIoU) loss function is proposed, which utilizes a dynamic non‐monotonic focusing mechanism to increase the penalty on distance measurement, thus enhancing the model's detection performance. Experiment findings on the ZJU‐Leaper and Tianchi textile datasets reveal that compared to the YOLOv7 baseline, the method in this paper has an increase of 6.5 and 2 percentage points, respectively, and the detection accuracy is better than most existing networks. [ABSTRACT FROM AUTHOR]

In the age of artificial intelligence, remote sensing and especially satellite imagery are gaining widespread interest among the computer science community in their efforts to enable machines to recognize their environment through satellite image classification. Imaging satellites provide images of Earth that are collected, analyzed, and processed for both civil and military purposes. Satellite images are an important source of data, captured by artificial satellites orbiting the Earth. These images are susceptible to noise and irregular illumination, which can affect their quality. This paper proposes an improved enhancement technique that increases the visual perception of the image while preserving its details. The proposed method uses image processing techniques with contrast enhancement to improve image quality. By enhancing contrast, this technique significantly benefits the creation of high-quality images. The effectiveness of the proposed method is evaluated using PSNR, entropy, and histogram analysis. [ABSTRACT FROM AUTHOR]

Agriculture is the most important sector of the Indian economy. Rice cultivation plays an important role in many regions of India. Most farmers in India are fully dependent on rice. Early detection of diseases in rice plants plays an important role in yielding more. This paper proposes a solution to detect and classify rice plant diseases too early using automatic image processing techniques. Automatic detection uses image segmentation and neural networks for classification of plant leaves. It takes the image as input and applies techniques to that image, like pre-processing and segmentation, and then the input is given to the convolutional neural network in order to classify the disease. Most Indian farmers are not well educated to detect the disease of the plant before it gets damaged, which results in less production. Rice production has the main role in Indian economics, so adequate efforts are needed to improve it. [ABSTRACT FROM AUTHOR]

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes. [ABSTRACT FROM AUTHOR]

This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimating their distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications in numerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring, and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructural projects. In this paper, we combine Gauss optical principles with the PTZ camera's capabilities of horizontal and pitch rotation, as well as optical zoom, to estimate the distance of the object.We present a novel monocular object distance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracy rate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplified convolution kernel and the utilization of the object's RGB features, which offer significantly faster computing speeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel before the fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distance estimation. Our system offers the advantages of stability and low device load, making it an asset for public security affairs and providing a reference point for future developments in surveillance hardware. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

Approximate computing is an emerging paradigm that can tolerate some loss of accuracy to improve the energy consumption and design complexity. Multiplication is one of the important computational components in various digital signal processing (DSP) applications. Improvement of multipliers, as the building blocks of an approximate computing systems, has received significant attention. Compressors are key elements in an approximate multiplier to have an optimal design with high performance. This paper proposes an approximate/exact 4–2 compressor and an exact 5–2 compressor in carbon nanotube field-effect transistors (CNTFET) technology for utilisation in an approximate multiplier. Four efficient designs for utilising the proposed compressors are introduced and analysed for a Wallace multiplier. All of the proposed circuits are analysed in 32 nm CNTFET Stanford technology and are simulated by HSPICE. In addition, as an image processing application of approximate multipliers, this paper compares the quality metrics of the image multiplication for the proposed designs and previously presented approximated multipliers. For example, the proposed schemes provide excellent results in structural similarity index up to 99%, and peak signal-to-noise ratio more than 45 dB. Also, it uses figure of metric (FOM) that includes the accuracy and quality metrics of the approximate multipliers. The simulation results demonstrate the efficiency of proposed designs. [ABSTRACT FROM AUTHOR]