Search Results (48,962)

Recently, zero-shot image captioning (ZSIC) has gained significant attention, given its potential to describe unseen objects in images. This is important for real-world applications such as human–computer interaction, intelligent education, and service robots. However, the zero-shot image captioning method based on large-scale pretrained models may generate descriptions containing objects that are not present in the image, which is a phenomenon termed “object hallucination”. This is because large-scale models tend to predict words or phrases with high frequency, as seen in the training phase. Additionally, the method set a limitation to the description length, which often leads to an improper ending. In this paper, a novel approach is proposed to address and reduce the object hallucination and improper ending problem in the ZSIC task. We introduce additional emotion signals as guidance for sentence generation, and we find that proper emotion will filter words that do not appear in the image. Moreover, we propose a novel strategy that gradually extends the number of words in a sentence to confirm the generated sentence is properly completed. Experimental results show that the proposed method achieves the leading performance on unsupervised metrics. More importantly, the subjective examples illustrate the effect of our method in improving hallucination and generating properly ending sentences. Full article

In inverse synthetic aperture radar (ISAR) systems, image quality often suffers from the non-uniform rotation of non-cooperative targets. Rotational motion compensation (RMC) is necessary to perform refocused ISAR imaging via estimated rotational motion parameters. However, estimation errors tend to accumulate with the estimated processes, deteriorating the image quality. A novel RMC algorithm is proposed in this study to mitigate the impact of cumulative errors. The proposed method uses an iterative approach based on a novel criterion, i.e., the minimum residual norm of the signal phases, to estimate different rotational parameters independently to avoid the issue caused by cumulative errors. First, a refined inverse function combined with interpolation is proposed to perform the RMC procedure. Then, the rotation parameters are estimated using an iterative procedure designed to minimize the residual norm of the compensated signal phases. Finally, with the estimated parameters, RMC is performed on signals in all range bins, and focused images are obtained using the Fourier transform. Furthermore, this study utilizes simulated and real data to validate and evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm shows dominance in the aspects of estimation accuracy, entropy values, and focusing characteristics. Full article

Dye-based angiography is the main imaging modality in evaluating the vasculature of the eye. Although most commonly used to assess retinal vasculature, it can also delineate normal and abnormal blood vessels in the anterior segment diseases—but is limited due to its invasive, time-consuming methods. Thus, anterior segment optical coherence tomography angiography (AS-OCTA) is a useful non-invasive modality capable of producing high-resolution images to evaluate the cornea and ocular surface vasculature. AS-OCTA has demonstrated the potential to detect and delineate blood vessels in the anterior segment with quality images comparable to dye-based angiography. AS-OCTA has a diverse range of applications for the cornea and ocular surface, such as objective assessment of corneal neovascularization and response to various treatments; diagnosis and evaluation of ocular surface squamous neoplasia; and evaluation of ocular surface disease including limbal stem cell deficiency and ischemia. Our review aims to summarize the new developments and clinical applications of AS-OCTA for the cornea and ocular surface. Full article

Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computation-in-memory (CIM), which computes multiplication and accumulations on memory arrays in an analog fashion, namely, analog CIM, we can further improve the energy efficiency to process neural networks. However, analog CIMs are susceptible to process variation, which refers to the variability in manufacturing that causes fluctuations in the electrical properties of transistors, resulting in significant degradation in BNN accuracy. Our Monte Carlo simulations demonstrate that in an SRAM-based analog CIM implementing the VGG-9 BNN model, the classification accuracy on the CIFAR-10 image dataset is degraded to below 50% under process variations in a 28 nm FD-SOI technology. To overcome this problem, we present a variation-aware BNN framework. The proposed framework is developed for SRAM-based BNN CIMs since SRAM is most widely used as on-chip memory; however , it is easily extensible to BNN CIMs based on other memories. Our extensive experimental results demonstrate that under process variation of 28 nm FD-SOI, with an SRAM array size of $128\times 128$, our framework significantly enhances classification accuracies on both the MNIST hand-written digit dataset and the CIFAR-10 image dataset. Specifically, for the CONVNET BNN model on MNIST, accuracy improves from 60.24% to 92.33%, while for the VGG-9 BNN model on CIFAR-10, accuracy increases from 45.23% to 78.22%. Full article

The aim of this communication article is to present a successful irrigation advisory scheme on the island of Crete (Greece) provided by the Hellenic Agricultural Organization (ELGO DIMITRA), which is well adapted to the different needs of farmers and water management agencies. The motivation to create this advisory scheme stems from the need to save water resources while ensuring optimal production in a region like Crete where droughts seem to occur more and more frequently in recent years. This scheme/approach has three different levels of implementation (components) depending on the spatial level and end-users’ needs. The first level concerns the weekly irrigation bulletins in the main agricultural areas of the island with the aim of informing farmers and local water managers about crop irrigation needs. The second level concerns an innovative digital web-based platform for the precise determination of the irrigation needs of Crete’s crops at a parcel level as well as optimal adaptation strategies in the context of climate change. In this platform, important features such as real-time meteorological information, spatial data on the cultivation type of parcels, validated algorithms for calculating crop irrigation needs, an accurate soil texture map derived from satellite images, and appropriate agronomic practices to conserve water based on cultivation and the geomorphology of a farm are considered. The third level of the proposed management approach includes an open-source Internet of Things (IoT) intelligent irrigation system for optimal individual parcel irrigation scheduling. This IoT system includes soil moisture and atmospheric sensors installed on the field, as well as the corresponding laboratory soil hydraulic characterization service. This third-level advisory approach provides farmers with specialized information on the automated irrigation system and optimization of irrigation water use. All the above irrigation advisory approaches have been implemented and evaluated by end-users with a very high degree of satisfaction in terms of effectiveness and usability. Full article

This study identifies the characteristics of water regions with negative normalized water-leaving radiance ($n{L}_w\left(\lambda \right)$) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative $n{L}_w\left(\lambda \right)$ values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative $n{L}_w\left(\lambda \right)$ values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of $n{L}_w\left(\lambda \right)$ and the single-scattering albedo ($\mathsf{\omega }\left(\lambda \right)$) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI $n{L}_w\left(\lambda \right)$, indicate the occurrence of negative values in SGLI $n{L}_w\left(\lambda \right)$ in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by $\mathsf{\omega }\left(\lambda \right)$ at 443 nm alone. Additionally, a comparison of in situ $n{L}_w\left(\lambda \right)$ measurements in Tokyo Bay and the Ariake Sea with $n{L}_w\left(\lambda \right)$ values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative $n{L}_w\left(\lambda \right)$ values are more likely to occur. These regions also tend to have lower $n{L}_w\left(\lambda \right)$ values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative $n{L}_w\left(\lambda \right)$ values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative $n{L}_w\left(\lambda \right)$ values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances. Full article

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods. Full article

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included. Full article

Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach. Full article

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots. Full article

The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convolutional neural networks and transformers. However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. This paper introduces a novel U-Net-like network for UAV image segmentation. We developed a link aggregation Mamba at the critical skip connection stage of UNetFormer. This approach maps and aggregates multi-scale features from different stages into a unified linear dimension through four Mamba branches containing state-space models (SSMs), ultimately decoupling and fusing these features to restore the contextual relationships in the mask. Moreover, the Mix-Mamba module is incorporated, leveraging a parallel self-attention mechanism with SSMs to merge the advantages of a global receptive field and reduce modeling complexity. This module facilitates nonlinear modeling across different channels and spaces through multipath activation, catering to international and local long-range dependencies. Evaluations on public remote sensing datasets like LovaDA, UAVid and Vaihingen underscore the state-of-the-art performance of our approach. Full article

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively. Full article

Background/Objectives: Atypical parkinsonian syndromes (APSs) are a group of neurodegenerative disorders that differ from idiopathic Parkinson’s disease (IPD) in their clinical presentation, underlying pathology, and response to treatment. APSs include conditions such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), and dementia with Lewy bodies (DLB). These disorders are characterized by a combination of parkinsonian features and additional symptoms, such as autonomic dysfunction, supranuclear gaze palsy, and asymmetric motor symptoms. Many hypotheses attempt to explain the causes of neurodegeneration in APSs, including interactions between environmental toxins, tau or α-synuclein pathology, oxidative stress, microglial activation, and vascular factors. While extensive research has been conducted on APSs, there is a limited understanding of the symmetry in these diseases, particularly in MSA. Neuroimaging studies have revealed metabolic, structural, and functional abnormalities that contribute to the asymmetry in APSs. The asymmetry in CBS is possibly caused by a variable reduction in striatal D2 receptor binding, as demonstrated in single-photon emission computed tomography (SPECT) examinations, which may explain the disease’s asymmetric manifestation and poor response to dopaminergic therapy. In PSP, clinical dysfunction correlates with white matter tract degeneration in the superior cerebellar peduncles and corpus callosum. MSA often involves atrophy in the pons, putamen, and cerebellum, with clinical symmetry potentially depending on the symmetry of the atrophy. The aim of this review is to present the study findings on potential symmetry as a tool for determining potential neuropsychological disturbances and properly diagnosing APSs to lessen the misdiagnosis rate. Methods: A comprehensive review of the academic literature was conducted using the medical literature available in PubMed. Appropriate studies were evaluated and examined based on patient characteristics and clinical and imaging examination outcomes in the context of potential asymmetry. Results: Among over 1000 patients whose data were collected, PSP-RS was symmetrical in approximately 84% ± 3% of cases, with S-CBD showing similar results. PSP-P was symmetrical in about 53–55% of cases, while PSP-CBS was symmetrical in fewer than half of the cases. MSA-C was symmetrical in around 40% of cases. It appears that MSA-P exhibits symmetry in about 15–35% of cases. CBS, according to the criteria, is a disease with an asymmetrical clinical presentation in 90–99% of cases. Similar results were obtained via imaging methods, but transcranial sonography produced different results. Conclusions: Determining neurodegeneration symmetry may help identify functional deficits and improve diagnostic accuracy. Patients with significant asymmetry in neurodegeneration may exhibit different neuropsychological symptoms based on their individual brain lateralization, impacting their cognitive functioning and quality of life. Full article

Tea polyphenols (TPs) are a critical indicator for evaluating the quality of tea leaves and are esteemed for their beneficial effects. The non-destructive detection of this component is essential for enhancing precise control in tea production and improving product quality. This study developed an enhanced PKO-SVR (support vector regression based on the Pied Kingfisher Optimization Algorithm) model for rapidly and accurately detecting tea polyphenol content in Fu brick tea using hyperspectral reflectance data. During this experiment, chemical analysis determined the tea polyphenol content, while hyperspectral imaging captured the spectral data. Data preprocessing techniques were applied to reduce noise interference and improve the prediction model. Additionally, several other models, including K-nearest neighbor (KNN) regression, neural network regression (BP), support vector regression based on the sparrow algorithm (SSA-SVR), and support vector regression based on particle swarm optimization (PSO-SVR), were established for comparison. The experiment results demonstrated that the improved PKO-SVR model excelled in predicting the polyphenol content of Fu brick tea (R² = 0.9152, RMSE = 0.5876, RPD = 3.4345 for the test set) and also exhibited a faster convergence rate. Therefore, the hyperspectral data combined with the PKO-SVR algorithm presented in this study proved effective for evaluating Fu brick tea’s polyphenol content. Full article

Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8’s capability for multi-scale feature extraction and multi-level feature fusion. Secondly, the CARAFE module, which is feature content-aware, was introduced to replace the up-sampling layer in YOLOv8n, thereby enhancing the model’s feature map reconstruction ability. Finally, an additional small-object detection layer was added to improve the detection accuracy of small defects. Simulation results indicate that YOLOv8-DCP achieves an accuracy of 97.7% and an [email protected] of 93.9%. Compared to YOLOv5, YOLOv7, and YOLOv8n, the accuracy improved by 1.5%, 4.3%, and 4.8%, while the [email protected] increased by 3.0%, 4.3%, and 3.1%. This results in a significant enhancement in the accuracy of insulator fault diagnosis. Full article