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Ordered Weighted Averaging (OWA) operators are a family of aggregation functions for data fusion. If the data are real numbers, then OWA operators can be characterized either as a special kind of discrete Choquet integral or simply as an arithmetic mean of the given values previously ordered. This paper analyzes the possible generalizations of these characterizations when OWA operators are defined on a complete lattice. In addition, the set of all n-ary OWA operators is studied as a sublattice of the lattice of all the n-ary aggregation functions defined on a distributive lattice. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field. [ABSTRACT FROM AUTHOR]

In this paper, we consider the issue of digital camera identification based on images. This topic matches the area of digital forensics. The problem is well known in the literature and many algorithms based on camera's fingerprint have been proposed. In this paper, we discuss the digital camera identification based on convolutional neural networks (CNN). CNNs are state-of-the-art method in computer vision and are widely utilized in many applications. Our goal is to find out whether it is possible to speed up the process of learning the networks by the images. We conduct a set of representative experiments which show that replacing the ReLU with SELU activation function and adjusting the network's hyperparamethers (e.g. learning rate) may have a significant impact on reduction time of learning. We also consider using the dropout layer. The experiments are held on representative image dataset, consisting of many images coming from modern cameras and show effectiveness of our propositions. [ABSTRACT FROM AUTHOR]

As an infrastructure of biochemical laboratories, EP tube label plays a significant role in information extraction to meet the limitations of computing power in offline devices and solve the problem that the EP tube label cannot be accurately identified before identification because the label belongs to multi-angle random placement. This paper proposes a light-weight neural network YOLOv4-tiny-ECA to position tubes and a tilt correction method based on Hough transform. First, the EP tube rack is roughly positioned based on the diffuse filling algorithm combined with digital morphological corrosion, and then the EP tubes in the rack are precisely positioned using the light-weight YOLO target detection algorithm combined with the attention mechanism. Next, the baseline is added to the label as the basis for determining the tilt angle. For the valid target, the baseline is extracted using the Hough transform and the tilt angle is calculated by vector fork multiplication. Finally, baseline is removed using image processing algorithm for better recognition results. Our results show that the light-weight YOLO algorithm reduces the network parameters by 56% and computation by 55% while keeping the accuracy rate largely unchanged, the offline positioning tilt correction method can achieve 98.8% accuracy and 0.076 s processing speed for a single test tube on average, which meets the real-time requirement. [ABSTRACT FROM AUTHOR]

The paper considers a class of discrete-time cellular neural networks (DT-CNNs) obtained by applying Euler's discretization scheme to standard CNNs. Let T be the DT-CNN interconnection matrix which is defined by the feedback cloning template. The paper shows that a DT-CNN is convergent, i.e. each solution tends to an equilibrium point, when T is symmetric and, in the case where T + E n is not positive-semidefinite, the step size of Euler's discretization scheme does not exceed a given bound ( E n is the n × n unit matrix). It is shown that two relevant properties hold as a consequence of the local and space-invariant interconnecting structure of a DT-CNN, namely: (1) the bound on the step size can be easily estimated via the elements of the DT-CNN feedback cloning template only; (2) the bound is independent of the DT-CNN dimension. These two properties make DT-CNNs very effective in view of computer simulations and for the practical applications to high-dimensional processing tasks. The obtained results are proved via Lyapunov approach and LaSalle's Invariance Principle in combination with some fundamental inequalities enjoyed by the projection operator on a convex set. The results are compared with previous ones in the literature on the convergence of DT-CNNs and also with those obtained for different neural network models as the Brain-State-in-a-Box model. Finally, the results on convergence are illustrated via the application to some relevant 2D and 1D DT-CNNs for image processing tasks. [ABSTRACT FROM AUTHOR]

This paper discusses about the modern techniques implemented in the field of agriculture which shaped the traditional farming to the smart farming (Agriculture 4.0). The rapid rise in the global population is one among the main reasons, besides monitoring of crops health and yield requires a huge labor force, which is also another reason for promoting intelligent systems into agriculture sector. Conventional farming methods are not suitable to meet this demand, which led robotics to associate with on-field agriculture by means of robot-based technologies like wheeled robots, ground vehicles (manned and unmanned) and aerial vehicles (manned and unmanned) that led to explore the possible advancement in agriculture 3.0. Currently, the evolutionary techniques, such as Artificial Intelligence (AI) and IoT, are implemented in robotic vehicles to make them intelligent systems. Due to unprecedented climatic change and polluted ground water for the past few decades, the crops are being infested with new varieties of diseases. This requires new image processing techniques to classify the diseases based on color, texture and, shape of leaves. The incorporation of image processing technique into AI aids in deciding the appropriate amount of herbicide supplement to the plant based on the prediction of plant growth. [ABSTRACT FROM AUTHOR]

Computer vision and optical character recognition techniques have rapidly advanced in order to accurately capture text and other features from paper documents. While state-of-the-art tools in these fields now yield high accuracy, analyzing their outputs requires more research. Since tables are common in such documents, a new pipeline, based on multimodal learning, is proposed to better extract key–value pairs from tables. Its performance is evaluated with a synthetically generated dataset with randomly generated tables and a dataset of mechanical part documents provided by SiliconExpert Technologies. Its performance is also compared with another state-of-the-art model built for similar tasks, LayoutLM. The proposed pipeline provides a fully automated, end-to-end scalable solution, beginning with image processing and computer vision components to a machine learning model that uses data from optical character recognition and natural language processing to make the final decisions. In the best configuration, the pipeline achieved a 96.26% accuracy on a large, synthetically generated training and test set. When comparing the proposed pipeline with LayoutLM, the proposed pipeline performed similarly on the synthetic dataset and better on the real dataset. These results show the potential of the multimodal approach in extracting key–value pairs from tables from real paper documents. [ABSTRACT FROM AUTHOR]

In recent days, the major concern for diabetic patients is foot ulcers. According to the survey, among 15 people among 100 are suffering from this foot ulcer. The wound or ulcer found which is found in diabetic patients consumes more time to heal, also required more conscious treatment. Foot ulcers may lead to deleterious danger condition and also may be the cause for loss of limb. By understanding this grim condition, this paper proposes Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) technique for analyzing foot ulcer 2D color images. This paper deals with standard image processing, i.e. efficient segmentation using FO-DPSO algorithm and extracting textural features using Gray Level Co-occurrence Matrix (GLCM) technique. The whole effort projected results as accuracy of 91.2%, sensitivity of 100% and specificity as 96.7% for Naïve Bayes classifier and accuracy of 91.2%, sensitivity of 100% and sensitivity of 79.6% for Hoeffding tree classifier. [ABSTRACT FROM AUTHOR]

To achieve low power consumption and high performance, an approximate multiplier is a commonly used operation. A new approximate computation method has been used for Error Tolerant Image Processing applications. This operation is popular in error tolerance applications. Inexact computing is applied to error-tolerant digital signal processing applications where error can be tolerated. Multiplier is the basic unit of arithmetic logic unit of any computer computational unit. In this paper, a new approximate compressor computation method is proposed for error-tolerant image processing applications. An approximate compressor has been designed for better output power with an improved figure of merit. A comparison of the proposed compressor with the previous 4:2 compressor design has been investigated and a reduction in area, delay and power consumption has been achieved. A similar algorithm has been applied to 8-bit multiplier applications which have considerable error performance. With the newly designed multiplier using a compressor, the simulation results compared the best power utilization. The results of designing the multiplier indicate that the proposed design of a multiplier using an approximate compressor achieves a reduction in power by 129 mw. [ABSTRACT FROM AUTHOR]

With the development of modern agricultural facilities, crop diseases recognition, nutritional status and morphology achieved rapid growth. To avoid yield loss caused by the delay of disease detection, digital images that contain information with respect to crop growth, disease type and nutrition deficiency have been studied by some researchers. However, traditional image processing methods fail to extract typical disease features of crop images with ambiguous disease information. In this paper, a crop disease image recognition technique based on the salient region and hierarchical indexing was proposed. Improved Harris algorithm and maximum radius were used to calculate the widest salient region. In order to eliminate the effect of different salience distribution ranges between different features, a group of images in the cucumber disease image library were normalized. Experiment results indicate that the time complexity of each algorithm will go up as the size of the dataset increase. Especially when testing large datasets, nonhierarchical and nonclustering, hierarchical and nonclustering and hierarchical based on points all tend to raise the algorithm's time complexity. Plant Village dataset and AI Challenger 2018 dataset were utilized to compare the recognition performances among the models based on machine learning, neural network, deep learning and our methods. The experiment results show that the method proposed in this paper is capable of recognizing local similar images effectively rather than global similar images, therefore, it has better recognition performance than the model learning methods in the early detection stage of crop disease. [ABSTRACT FROM AUTHOR]

The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37% for DRIVE with a runtime of 1.77 s and 95.17% for STARE with 2.05 s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant. [ABSTRACT FROM AUTHOR]

High-dimensional data that often lie in low-dimensional subspaces are ubiquitous in many fields of signal and image processing, pattern recognition, machine learning, etc. Finding sparse representations of data points in a dictionary built by using the collection of data helps to study low-dimensional subspaces. In this paper, we consider the problem of subspace-sparse representation for data composed of two distinct features. Applying different measures to the coefficients of the two distinct features under different dictionaries makes the model used in this paper more general. We present theoretical guarantee for subspace-sparse representation under conditions on the subspaces and data. The program used in this paper can handle data with structured corruption, missing entries, sparse outliers and random bounded noise well. [ABSTRACT FROM AUTHOR]

Measuring image visual quality is extremely important for many image processing tasks. In the past, several metrics have been proposed for measuring image visual quality such as structural similarity index (SSIM) and visual information fidelity (VIF). Nevertheless, these metrics are not robust to image spatial shifts when the reference and distorted images are misaligned by a few pixels. These metrics generate extremely low metric scores which is undesirable. It is well known that shifting the image by a few pixels does not affect the perceived image quality significantly. In this paper, we modify the SSIM metric to make it more robust to spatial shifts by pre-processing the input images with two-dimensional (2D) Fast Fourier Transform (FFT2). We then use the magnitudes of the Fourier coefficients in the existing metrics since these coefficients are shift-invariant. Experiments show that our proposed novel method is particularly good at measuring the visual quality of 2D images because it is far less complex than the existing methods and it offers better accuracy. Our new method is better than SSIM even when no spatial shifts are introduced to the images. [ABSTRACT FROM AUTHOR]

Approximate multipliers have gradually become a focus of research due to the emergence of fault-tolerant applications. This paper deals with the approximation methods for an approximation multiplier with truncation, probability transformation and a majority gate-based compressor chain. With the help of probability analysis, the proposed approximation methods are utilized in an approximate 8 × 8 unsigned multiplier to achieve low accuracy loss, high efficiency for time and power. Compared with the precise and approximate multipliers, the proposed design brings 55.0%, 39.0% reduction in delay and 73.8%, 22.6% power saving. The proposed multiplier achieves better peak signal-to-noise ratio (PSNR) values when evaluated with an image processing application. [ABSTRACT FROM AUTHOR]

Image segmentation is an essential part of medical image processing, which plays a significant role in adjunctive therapy, disease diagnosis, and medical assessment. To solve the problem of insufficient extracting context information, especially for medical image segmentation, this paper proposes a novel network architecture of multi-scale object context dilated transformer network (Multi-OCDTNet) to improve the utilization and segmentation accuracy for context information. The multi-scale object context transformer module can extract the multi-scale context information of the image through a three-layer transformer structure in a parallel way. The dilated convolution self-aware module can enhance the awareness of multi-scale context information in the feature map through layering transformer block groups and a set of transformer layers. In addition, we propose a composite weight-assigned-based loss function based on DDCLoss and Focal Tversky Loss to improve the stability of the segmentation performance of Multi-OCDTNet by adjusting the weight. The performance of Multi-OCDTNet is validated on the DRIVE and STARE datasets with segmentation accuracy of 97.17% and 97.84%, respectively, indicating the Multi-OCDTNet network possesses a significant competitive advantage in improving the segmentation performance of retinal vessel images. [ABSTRACT FROM AUTHOR]

Addition and multiplication are some of the most broadly adopted arithmetic operations in a wide range of applications. This paper proposes new structures of approximate multipliers to optimize the area, delay, and power without affecting the accuracy metrics. Multipliers and adders play a significant role in the functioning of any digital circuit or system. The overall performance of a processor highly depends on the speed of adders and the energy consumption. In this paper, two types of compact error-tolerant approximate adders are designed and used along with approximate 4:2 compressors to improvise the efficiency of the approximate multipliers. The proposed approximate multipliers show good results when compared to the existing structures in terms of area, delay, power, and accuracy. The approximate multipliers are applied to image sharpening and image multiplication applications. The error-tolerant adder's performance is evaluated in the practical domain using the image blending application. Peak signal-to-noise ratio (PSNR) performance and the structural similarity index metric (SSIM) are used to assess the modeled designs. The proposed approximate multipliers and adders exhibit better performance in terms of PSNR and SSIM and are found to be an optimized design to apply effectively in various error-tolerant image processing applications. [ABSTRACT FROM AUTHOR]

The mathematical skeleton of a complex form has been essential for a variety of scientific fields and of great interest to many researchers for decades. It is based on several concepts such as the reconstruction of forms and image processing. This paper aims to develop a novel mathematical algorithm to approximate the skeleton of a non-polygonal shape and to compare it to the most used methods. The mathematical technique of skeletonization is used as a reference to validate and compare the proposed method to the most used ones. The crux of the proposed technique is to Cartesianize the shape (polygonize in 2D), then skeletonize it. Moreover, this novel method is grounded upon the construction of bisectors on the simplex of the corresponding Cartesianized shape. Python is used to implement the algorithm proposed and test it on multiple shapes. The comparison of the results generated by the proposed algorithm and the Python predefined function skeletonize() shows that the proposed method is more effective and could be adjusted through the rate of Cartesianization of the target shape. The major contributions of this novel technique include the mitigation of some issues of existing methods, simplification, and optimization of the processing performance mainly in terms of algorithm complexity. [ABSTRACT FROM AUTHOR]

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient's cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of 3 2 8 2 drawings. The proposed method provides an accuracy of 7 5. 6 5 % in the binary case-control classification task, with an AUC of 0. 8 3. These results are indeed relevant considering the use of the classic version of the CDT. The large size of the sample suggests that the method proposed has a high reliability to be used in clinical contexts and demonstrates the suitability of CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by CI. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed. [ABSTRACT FROM AUTHOR]

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue. [ABSTRACT FROM AUTHOR]

Feature extraction and matching are the basic procedures of the so-called Visual Odometer (VO), Simultaneous Localization and Mapping (SLAM) and many other image processing algorithms. Oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm are widely used since they are computationally faster. In this paper, we proposed a method to generate a value for a feature, the value is called signature. In the matching step, we only compute Hamming distances of descriptors with the same signatures. Hence, the matching time is shortened. Compared with the original ORB algorithm, features to be matched dropped 69.63% on TUM datasets and 85.7% on VGG datasets by adopting our strategy. In addition, the precision is above 85% on both VGG and TUM datasets. We design a customized hardware architecture for ORB feature extraction and matching based on the proposed method. The hardware structure is implemented on Xilinx ZCU102 evaluation board. The clock frequency is set to 150 MHz. Our Field Programmable Gate Arrays (FPGA) system achieves 193 fps on 1 2 8 0 × 7 2 0 images with 1984 features on average and 314 fps on 6 4 0 × 4 8 0 images with 700 features on average, which is more efficient compared to the state-of-the-art works. [ABSTRACT FROM AUTHOR]

In order to solve the local overexposure caused by uneven surface reflectance, this paper proposes a fast-adaptive illumination control method with a camera-projector system. At first, an image is captured by the camera and the local overexposed area is segmented using saliency detection. Then the calculated image is projected onto the object by the projector as corrective illumination. The calculation process includes the inversion of the gray value in the overexposed area and the adjustment based on the position and depth information of the object. The high-exposure saturated regional which affects the target recognition is thus reduced, and the original illumination intensity is reserved for the other regions. This process is iterated until the optimal illumination is achieved. The resulting image for each iteration is evaluated using Blind/no Reference Image Space Quality Estimator (BRISQUE). When BRISQUE value reaches the minimum, a high-quality image is achieved. The experiments show that the proposed approach can significantly improve the speed of obtaining normally exposed images, and this system provides new ideas for industry image acquisition. [ABSTRACT FROM AUTHOR]

Optical character recognition (OCR) is a computer vision technique that enables computers to recognize text from images. Text detection and computer vision have made significant advancements, leading to the development of various OCR technologies. However, selecting the most suitable OCR system for a specific purpose has become a challenging task. This research paper aims to explain the theoretical concepts and mathematical formulas underlying OCR engines, providing a better understanding of their functioning and performance. The analysis covers various aspects, including the theories, algorithms, and techniques employed by OCR engines. This paper presents experiments conducted on five different image categories: vehicle number plates, receipts, handwriting, symbols and plain text images. Evaluation metrics such as Character Error Rate (CER), Word Error Rate (WER), Insertion Error Rate (IER), Deletion Error Rate (DER), End-to-end recognition rate (EEER), Word Error Rate (WER), Recall, Precision, and F1-score were utilized. The findings reveal that OCR systems perform well on plain documents, with recall and F1-score values exceeding 0.85 and 0.8, respectively. However, there are still areas for improvement in OCR, which are discussed in detail in this paper. This research provides valuable insights for researchers, developers, and practitioners interested in employing OCR technology for their commercial projects. [ABSTRACT FROM AUTHOR]

Some people in society have impaired cognitive senses like speech and hearing where they cannot behave like normal people. It is quite a complex task for abnormal people to understand as well as recognize the gestures of normal people. This initiates to delve into the study of review of Sign Language Recognition (SLR), in specific to, machine learning techniques. In this work, a review of machine learning techniques based on SLR were portrayed. Several studies related to ML papers have been collected and discussed with their merits and demerits. Thus, the observation dictates that recognition of hand gesture is still a challenging task. There are two sorts of gesture recognition, namely, static and dynamic gesture recognition. Static gesture recognition is developed from the dynamic gesture recognition. Almost, Convolutional Neural Networks (CNNs), Hidden Markov Models (HMM) and Histogram analysis were used as recognition classifiers for sign language. Dynamic gesture recognition process operates on tracking the centroid of hand gesture. It changes the visual information in time basis. Henceforth, study on dynamic gesture recognition needs to be more focused using Machine learning techniques. Comparative analysis is done in perspectives of significance of segmentation models, feature extraction and vision-based approaches. [ABSTRACT FROM AUTHOR]

Real-time and accurate measurement of coal quantity is the key to energy-saving and speed regulation of belt conveyor. The electronic belt scale and the nuclear scale are the commonly used methods for detecting coal quantity. However, the electronic belt scale uses contact measurement with low measurement accuracy and a large error range. Although nuclear detection methods have high accuracy, they have huge potential safety hazards due to radiation. Due to the above reasons, this paper presents a method of coal quantity detection and classification based on machine vision and deep learning. This method uses an industrial camera to collect the dynamic coal quantity images of the conveyor belt irradiated by the laser transmitter. After preprocessing, skeleton extraction, laser line thinning, disconnection connection, image fusion, and filling, the collected images are processed to obtain coal flow cross-sectional images. According to the cross-sectional area and the belt speed of the belt conveyor, the coal volume per unit time is obtained, and the dynamic coal quantity detection is realized. On this basis, in order to realize the dynamic classification of coal quantity, the coal flow cross-section images corresponding to different coal quantities are divided into coal type images to establish the coal quantity data set. Then, a Dense-VGG network for dynamic coal classification is established by the VGG16 network. After the network training is completed, the dynamic classification performance of the method is verified through the experimental platform. The experimental results show that the classification accuracy reaches 94.34%, and the processing time of a single frame image is 0.270 s. [ABSTRACT FROM AUTHOR]

Foundation models (FMs) have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic (PA) image processing. We employed the Segment Anything Model (SAM) by setting simple prompts and integrating the model’s outputs with prior knowledge of the imaged objects to accomplish various tasks, including: (1) removing the skin signal in three-dimensional PA image rendering; (2) dual speed-of-sound reconstruction, and (3) segmentation of finger blood vessels. Through these demonstrations, we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training. This potentially allows for a hands-on, convenient approach to achieving efficient and accurate segmentation of PA images. This paper serves as a comprehensive tutorial, facilitating the mastery of the technique through the provision of code and sample datasets. [ABSTRACT FROM AUTHOR]

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

This research undertakes an exhaustive exploration of the Auto Machine Learning-based approach (AutoML) to ascertain the source printer for microscopic printed image forensics. The primary focus is on enhancing precision and efficacy in a scenario with limited sample data, achieved by integrating traditional image processing methods with diverse AutoML representatives. The investigation involves a meticulous evaluation and comparative analysis of classical Machine Learning (ML) models and AutoML models in microscopic printed image forensics. Through extensive experiments, we highlight the inherent limitations of traditional models and underscore the compelling advantages offered by AutoML. Notably, we recognize AutoML's unique capability to discern varying degrees of uncertainty within printed patterns. The outcomes emphasize the prowess of Auto Machine Learning in improving the accuracy of printed image forensics, making it particularly promising for future research, especially given the tabular nature of the data and the imperative for a lightweight model suitable for embedded systems. [ABSTRACT FROM AUTHOR]

The field of hexagonal image processing is concerned with the creation of image processing systems that combine the advantages of biological model-based evolutionary motivated frameworks. The structure and functionality of artificial neural networks were inspired by biological processes. The fundamental framework of recording and output devices limits their present state of the art. Prior neural networks have used square or hexagonal style input to completely connected layers, which resulted in a high coherence problem between two adjacent hexagonal kernel layers due to pooling. Previous research does not design the self-data structure to support convolution to increase computational efficiency, so it violates the convolution and pooling operator, which greatly degrades the image process performance. This paper introduces a novel paradigm Proficient Deep Learning-based Hexrep Neural Network that overcomes major significant problems in image operations structure constraint, coherence problem, and violation of convolution and pooling operator and achieves hexagonal image processing. [ABSTRACT FROM AUTHOR]

In order to be able to achieve state maintenance, keep historical data of the operation of each system, improve the precision of computer room management, improve work efficiency, reduce the work intensity of computer room managers, and reduce manual errors and potential accidents, this paper combines the Internet of Things technology to construct an image encryption transmission method that can be used for power monitoring. Moreover, this paper constructs a power system intelligent monitoring system based on the Internet of Things, and encrypts relevant data. In addition, this paper proposes a surveillance video encryption algorithm based on foreground and background separation. This video encryption algorithm is different from ordinary video encryption methods. It fully taps the characteristics of the surveillance video taken by a still camera. According to the experimental research results, it can be seen that the power monitoring image encryption transmission system based on the Internet of Things constructed in this paper has good performance in power monitoring image processing, image encryption, image transmission, etc. The evaluation results of the three functional parameters are all above 80%, which have great advantages compared with traditional algorithms. Therefore, the method proposed in this paper has good practical effects. [ABSTRACT FROM AUTHOR]

Electrophysiological signals can effectively reflect various physiological states of human body, and provide favorable basis for medical diagnosis. However, the correct analysis of electrophysiological signals requires professional medical diagnosis experience. With the rapid development of artificial intelligence, intelligent diagnosis methods based on deep learning are gradually applied in the medical field in order to reduce the dependence of diagnosis results on medical experience. Deep learning has made remarkable achievements in the field of image processing, through which deeper information can be extracted than through time-series signals. Therefore, this paper proposes a method of 2DECG diagnosis based on Faster R-CNN (Faster Region-based Convolutional Neural Network). First, the time-series ECG signal is transformed into two-dimensional curve. Then, the Faster R-CNN model based on beat is obtained by using dataset training. Finally, three kinds of ECG diseases are diagnosed by the Faster R-CNN model. The test results show that compared with the effect of one-dimensional CNN, the method proposed in this paper has high diagnosis accuracy and can help doctors to diagnose diseases more intuitively. [ABSTRACT FROM AUTHOR]

In the field of human–computer interaction, there is a trend toward using emotional interaction and putting emotions at the center. However, traditional facial expression recognition is greatly affected by lighting, skin color and make-up, and is not well recognized. This paper addresses these shortcomings in the following areas: (1) The algorithm of infrared face feature localization is studied, using a combination of region growing algorithm to highlight the organ features of infrared faces, and the Harris corner point detection algorithm to detect the organ features of human beings and locate the location of the large organs related to expressions, such as eyes, nose and mouth. (2) The algorithm for feature extraction is investigated, and the local binarization LBP algorithm is proposed to extract the texture features of the infrared face. The LBP algorithm is synthesized to extract the overall LBP features by applying the innovation of chunking and layering of features. By analyzing and processing the infrared face image data from both visible and infrared face expression data, feature detection, feature extraction and effective classification of the four basic face expressions — happy, angry, disgusted and sad — are performed on the infrared images. The application of the algorithm is processed and improved accordingly to the characteristics of infrared face images, which improves the accuracy of recognition and classification compared to traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents a one-level decomposition Haar Discrete Wavelet Transform (DWT) architecture using a 4:2 compressor and carry propagate adder. In Haar DWT architecture, coefficient multiplication is an essential operation. The Haar coefficient multiplication ((x even ± x odd ) ∗ 0. 7 0 7 1 0 6) is implemented with R a d i x − 2 r multiplier and the generated partial products are represented with sign power of two (SPT) terms. The addition of SPT terms is computed with a 4:2 compressor and the final sum is computed with CPA. A R a d i x − 2 r multiplier with 4:2 compressor technique is used to improve energy and delay. Compared to the previous architectures, the proposed architecture gives reduction in area, power, and delay. The proposed Haar wavelet architecture is implemented in gate-level Verilog HDL and synthesized with UMC 90-nm technology using Cadence RC compiler. When compared to the existing designs, the proposed architecture Haar DWT architecture synthesis results show reduction in latency of 32.32% and 31.46% of circuit area. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier. [ABSTRACT FROM AUTHOR]

As many digital signal processing (DSP) applications such as digital filtering are inherently error-tolerant, approximate computing has attracted significant attention. A multiplier is the fundamental component for DSP applications and takes up the most part of the resource utilization, namely power and area. A multiplier consists of partial product arrays (PPAs) and compressors are often used to reduce partial products (PPs) to generate the final product. Approximate computing has been studied as an innovative paradigm for reducing resource utilization for the DSP systems. In this paper, a 4:2 approximate compressor-based multiplier is studied. Approximate 4:2 compressors are designed with a practical design criterion, and an approximate multiplier that uses both truncation and the proposed compressors for PP reduction is subsequently designed. Different levels of truncation and approximate compression combination are studied for accuracy and electrical performance. A practical selection algorithm is then leveraged to identify the optimal combinations for multiplier designs with better performance in terms of both accuracy and electrical performance measurements. Two real case studies are performed, i.e., image processing and a finite impulse response (FIR) filter. The design proposed in this paper has achieved up to 16.96% and 20.81% savings on power and area with an average signal-to-noise ratio (SNR) larger than 25 dB for image processing; similarly, with a decrease of 0.3 dB in the output SNR, 12.22% and 30.05% savings on power and area have been achieved for an FIR filter compared to conventional multiplier designs. [ABSTRACT FROM AUTHOR]

Edge is basically the symbol and reflection of partial image discreteness. It is one of the most commonly used operations in image processing and pattern recognition, it contains a wealth of internal information leading to strong interpretation of image. Resisting against noise, illumination and extracting appropriate features from an image is a great challenge in many computer vision applications. Indeed this topic participates to reduce the handled information and focuses on those related to existing objects. Efficient and accurate edge detection will lead to increase in the performance of many computer vision applications, including image segmentation, object-based image coding and image retrieval. Contour detection contributes to locate pixel sets which correspond to sudden intensities variation, these unstable properties of the given image commonly suggest to important events on going in the scene. In this paper, we present in the first time a novel and robust method for edge detection based on joint and conditional entropy when we highlight a Shannon theory, the second part of this paper is dedicated to decision making of edge pixels membership by intelligent method based on fuzzy logic tool. [ABSTRACT FROM AUTHOR]

The activeness of regional business entities, like restaurants, cinemas and shopping malls, represents the evolvement of their corresponding commercial districts, whose prediction helps practitioners grasp the trend of commercial development and provides support for urban layout. On the other hand, online social network services, such as Yelp, are generating massive online reviews toward business entities every day, which provide a solid data source for the prediction of regional commercial activeness and entity condition through big data technology rather than applying business data with limited access and poor time efficiency. Inspired by the outstanding performance of deep learning in the field of image and video processing, this paper proposes a deep spatio-temporal residual network (DSTRN) model for regional commercial activeness prediction using online reviews and check-in records of commercial entities. Furthermore, aiming at predicting business trend of entities, we also propose a novel multi-view entity condition prediction model (SBCE) based on online views, along with business attributes and regional commercial activeness. The experiments on the public Yelp datasets demonstrate that both DSTRN and SBCE outperform the compared approaches. [ABSTRACT FROM AUTHOR]

As an important branch of artificial intelligence, computer vision plays a huge role in the rapid development of artificial intelligence. From a biological point of view, in the acquisition and processing of information, vision is much more important than hearing, touch, etc., because 70% of the human cerebral cortex is processing visual information. Therefore, advances in computer vision technology are critical to the development of artificial intelligence that is designed to allow machines to think and handle things like humans. The acquisition and processing of visual information has always been the focus of computer vision research, and it is also difficult. The main problem of traditional computer vision technology in the processing of visual information is that the extracted image features are less discriminative, the generalization ability of image features in complex background scenes is insufficient, and the recognition ability on object recognition is poor. In response to these problems, based on the visual neural mechanism, this paper establishes an appropriate computer model for the neuronal cells in the human primary visual cortex, models the recognition response mechanism of the visual ventral system, and performs image feature extraction on the training samples. And object recognition. The results show that compared with the traditional methods, the proposed method effectively improves the discrimination of image features, and the image features extracted under complex background scenes have good generalization ability. On this basis, the training samples can be effectively recognized. The results show that the model based on the visual neural mechanism, the recognition of the edge, orientation and contour of the training sample show the advantages of the biological vision mechanism in object recognition. [ABSTRACT FROM AUTHOR]

The medical image fusion process integrates the information of multiple source images into a single image. This fused image can provide more comprehensive information and is helpful in clinical diagnosis and treatment. In this paper, a new medical image fusion algorithm is proposed. Firstly, the original image is decomposed into a low-frequency sub-band and a series of high-frequency sub-bands by using nonsubsampled shearlet transform (NSST). For the low-frequency sub-band, kirsch operator is used to extract the directional feature maps from eight directions and novel sum-modified-Laplacian (NSML) method is used to calculate the significant information of each directional feature map, and then, combining a sigmod function and the significant information updated by gradient domain guided image filtering (GDGF), calculate the fusion weight coefficients of the directional feature maps. The fused feature map is obtained by summing the convolutions of the weight coefficients and the directional feature maps. The final fused low-frequency sub-band is obtained by the linear combination of the eight fused directional feature maps. The modified pulse coupled neural network (MPCNN) model is used to calculate the firing times of each high-frequency sub-band coefficient, and the fused high-frequency sub-bands are selected according to the firing times. Finally, the inverse NSST acts on the fused low-frequency sub-band and the fused high-frequency sub-bands to obtain the fused image. The experimental results show that the proposed medical image fusion algorithm expresses some advantages over the classical medical image fusion algorithms in objective and subjective evaluation. [ABSTRACT FROM AUTHOR]

LSS-Target (the Low altitude, Slow speed and Small Target) is likely to be a threat to the observation platform, thus infrared LSS-Target detection is an urgent task. LSS-Target is a challenging issue due to the low Signal-to-Noise Ratio (SNR) and sophisticated background. Motivated by the analysis of infrared imaging characteristics, this paper proposes a novel fusion method for IR LSS-Target detection with complex urban background, which is suitable for precise guidance and self defense. First, an adaptive threshold segmentation based on accumulative histogram and maximum likelihood estimation are utilized to eliminate the clutter and improve SNR of the initial image. Second, a template is set up to identify the seed points in the image. Third, a constrained four criteria region growth algorithm is performed to separate the entire regions. Finally, the confidence measure is constructed, which can eliminate false targets and the background edges. Experimental results show that the method in this paper can screen out the real LSS-Target in real time with high accuracy under sophisticated background. [ABSTRACT FROM AUTHOR]

Different kinds of illustrations and artistic imagery can be generated or simulated through the nonphotorealistic rendering (NPR) technique. However, designing and simulating new NPR artistic styles remains extremely challenging. Chalk art style is a very famous artistic work all over the world, and few algorithms have been put forward to illustrate this style. This paper presents a novel NPR technique which generates a chalk art drawing from a 2D photograph automatically. We aim at obtaining a set of lines surface with coarse appearance and generating stroke textures of the real chalk painting. Firstly, the edge of the source image is extracted by difference-of-Gaussian filter method. To simulate chalk painting's lines, image diffusion and enhancement techniques are proposed to produce coarse and rough lines. Secondly, we developed an improved line integral convolution and dilation operation methods to produce the chalk stroke texture. Finally, the edge image, stroke texture image and color image will be mapped to another background image to generate the chalk art drawing. Experimental results are presented to show the effectiveness of our method in producing the color chalk stylistic illustrations, and the methods can simulate the characters of the real chalk art painting. The proposed method of this paper will enlarge the research and application fields of NPR. Meanwhile, it provides a tool for the user to create chalk art paintings via computers even without painting skill. [ABSTRACT FROM AUTHOR]

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot's identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved; K -means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size; BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84 fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection. [ABSTRACT FROM AUTHOR]

Financial risk warning is a crucial technical issue for enterprises. Traditionally, it relied on modeling analysis of a single data type, which does not fully capture the diverse characteristics of financial risk activities. To address this, this paper introduces a multimodal deep learning framework driven by semantic analysis and image processing for intelligent warning of enterprise financial risks. Initially, natural language processing algorithms analyze textual data such as financial statements, news reports, and social media comments within the industry. Concurrently, two convolutional neural network models, M-CNN and M-RNN, extract features from images and chart data. These textual and visual feature representations are then fused to create a multimodal deep neural network framework. Extensive experimental evaluation and comparative analysis of the proposed framework were conducted. The results indicate that the financial risk rate of consumer fraud graph analysis varies significantly, displaying a fluctuating state with values ranging from 2.1% to 16.8%. Compared to other methods, the proposed approach demonstrates superior performance in financial risk warning tasks, with the risk rate increasing from 1.2% to 26.5% as it iterates from 1 to 6. [ABSTRACT FROM AUTHOR]

Proximal alternating direction method of multipliers (PADMM) is a classical primal-dual splitting method for solving separable convex optimization problems with linear equality constraints, which have numerous applications in, e.g., signal and image processing, machine learning, and statistics. In this paper, we propose a new variant of PADMM, called PADMC, whose proximal centers are constructed by convex combinations of the iterates. PADMC is able to take advantage of problem structures and preserves the desirable properties of the classical PADMM. We establish iterate convergence as well as (1 / K) ergodic and (1 / K) nonergodic sublinear convergence rate results measured by function residual and feasibility violation, where K denotes the iteration number. Moreover, we propose two fast variants of PADMC, one achieves faster (1 / K 2) ergodic convergence rate when one of the component functions is strongly convex, and the other ensures faster (1 / K) nonergodic convergence rate measured by constraint violation. Finally, preliminary numerical results on the LASSO and the elastic-net regularization problems are presented to demonstrate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

The double-proximal gradient algorithm (DPGA) is a new variant of the classical difference-of-convex algorithm (DCA) for solving difference-of-convex (DC) optimization problems. In this paper, we propose an accelerated version of the double-proximal gradient algorithm for DC programming, in which the objective function consists of three convex modules (only one module is smooth). We establish convergence of the sequence generated by our algorithm if the objective function satisfies the Kurdyka– Ł ojasiewicz (K Ł) property and show that its convergence rate is not weaker than DPGA. Compared with DPGA, the numerical experiments on an image processing model show that the number of iterations of ADPGA is reduced by 43.57% and the running time is reduced by 43.47% on average. [ABSTRACT FROM AUTHOR]

The fast scenario transfer for portrait styles has been a universal concern in the area of image processing. Conventionally, it was realized mostly by manual manipulation based on expertise experience, which costs a large amount of human labor. To fit the growing business volume in the era of Internet 4.0, it is supposed to investigate reliable autonomous workflow that generates portrait-style results via intelligent algorithms. Therefore, this paper proposes a fast scenario transfer method for portrait styles through collaborative awareness of convolutional neural network (CNN) and generative learning (GAL). On the one hand, a CNN structure is designed to make feature extraction towards the initial images, which can be used for following image generation. On the other hand, the GAL is utilized to generate new images with post-transferred scenario styles, so that the expected scenario style transfer results can be output by generating new images through long-term iterations. Especially, the loss function is designed by simultaneously considering local and global characteristics. Finally, the analysis and experimental results show that the correction process can be completed by injecting the extracted input face features into the feature maps of corresponding sizes in the generation process using a pre-trained feature extractor. Thus, the face conversion efficiency can be improved. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the capabilities of the inexpensive theodolite created by the authors, compared with modern commercial instruments, equipped with a Leica TPS1203 robotic total station with built-in GoPro Hero6 camera image sensors. This theodolite was tested in the laboratory where simulated dynamic displacements were determined. The results of the experimental tests showed that the displacement errors and differences between the simulated displacements of the test machine and those detected by the theodolite were in the range of Δ = − 0. 1 5 mm to +0.13 mm depending on the simulated amplitude. The theodolite was further used in the field for static and dynamic tests of the Wanzhou Railway Bridge, China. Determination of the dynamic motions of the bridge and the results of calculating the natural frequencies from the measurement data are presented. During loading tests of the bridge, the frequencies were also determined by accelerometers and these data were used as a reference to assess the accuracy of the theodolite and its suitability for dynamic tests. An original algorithm was developed in the MATLAB software environment to process the recorded videos, i.e. image processing to determine displacements and natural frequencies. It is demonstrated that the position of the theodolite at a distance of 28.5 m from the bridge provides the correspondence of one pixel of 0.333 mm, which provides a high level of accuracy when determining the dynamic vertical displacements of a moving target. It is established that the proposed theodolite will cost less than 5 000 euros. The results obtained are relevant for use in civil engineering for monitoring the amplitude vibrations of structures. [ABSTRACT FROM AUTHOR]

Nowadays, hyperspectral imaging (HSI) attracts the interest of many researchers in solving the remote sensing problems especially in various specific domains such as agriculture, snow/ice, object detection and environmental monitoring. In the previous literature, various attempts have been made to extract the critical information through hyperspectral imaging which is not possible through multispectral imaging (MSI). The classification in image processing is one of the important steps to categorize and label the pixels based on some specific rules. There are various supervised and unsupervised approaches which can be used for classification. Since the past decades, various classifiers have been developed and improved to meet the requirement of remote sensing researchers. However, each method has its own merits and demerits and is not applicable in all scenarios. Past literature also concluded that deep learning classifiers are more preferable as compared to machine learning classifiers due to various advantages such as lesser training time for model generation, handle complex data and lesser user intervention requirements. This paper aims to perform the review on various machine learning and deep learning-based classifiers for HSI classification along with challenges and remedial solution of deep learning with hyperspectral imaging. This work also highlights the various limitations of the classifiers which can be resolved with developments and incorporation of well-defined techniques. [ABSTRACT FROM AUTHOR]

Copy move forgery in digital images became a common problem due to the wide accessibility of image processing algorithms and open-source editing software. The human visual system cannot identify the traces of forgery in the tampered image. The proliferation of such digital images through the internet and social media is possible with a finger touch. These tampered images have been used in news reports, judicial forensics, medical records, and financial statements. In this paper, a detailed review has been carried on various copy-move forgery detection (CMFD) and localization techniques. Further, challenges in the research are identified along with possible solutions. [ABSTRACT FROM AUTHOR]

The holistic understanding of the information contained in technical documents depends on the understanding of the document's individual modalities. These modalities are tables, graphics, diagrams, formulas, etc. and each of them is a standalone research topic that requires a different way of processing and understanding. These modalities, processed and combined with the document text, can introduce new techniques for visual question answering in documents. Thus, in this paper we present Pinakas, a methodology for automatic analysis of the internal tabular information that appears in technical documents and its modeling to stochastic Petri-net graphs. We focus only on tables that strictly abide by the IEEE format rules. The methodology presented here is divided into the following steps: (1) table detection, (2) table recognition, (3) table understanding. Qualitative results of Pinakas are demonstrated as proof of concept for the accurate extraction of information from three different types of tables. [ABSTRACT FROM AUTHOR]