In this paper, we begin with the classical concept of tight frames in Hilbert spaces. First, we introduce the orthogonal projection P between H and θ(H) (the range of the frame transform θ associated with a traditional tight frame) and in...
Contrast-enhanced spectral mammography (CESM) provides low-energy images acquired in standard craniocaudal (CC) and mediolateral oblique (MLO) views, and clinical interpretation relies on integrating both views. This study proposes a dual-view classi...
For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task spac...
Intracranial aneurysms (IAs), a significant medical concern due to their prevalence and life-threatening nature, pose challenges regarding diagnosis owing to their diminutive and variable morphology. There are currently challenges surrounding automat...
Multimodal sentiment analysis (MSA) seeks to predict subjective human sentiments by utilizing information from multiple modalities. It has been applied in diverse scenarios. Recent studies suggest that MSA benefits from integrating diverse modalities...
Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNe...
In recent years, the powerful feature extraction capabilities of deep learning have attracted widespread attention in the field of bearing fault diagnosis. To address the limitations of single-modal and single-channel feature extraction methods, whic...
Multispectral object detection is a crucial technology in remote sensing image processing, particularly in low-light environments. Most current methods extract features at a single scale, resulting in the fusion of invalid features and the failure to...
Speech emotion recognition (SER) aims to recognize human emotions through in-depth analysis of audio signals. However, it remains challenging to encode emotional cues and to fuse the encoded cues effectively. In this study, dual-stream representation...
Depth information is crucial for perceiving three-dimensional scenes. However, depth maps captured directly by depth sensors are often incomplete and noisy, our objective in the depth-completion task is to generate dense and accurate depth maps from...
Sensor fusion is crucial in numerous imaging and sensing applications. Integrating data from multiple sensors with different field-of-view, resolution, and frame timing poses substantial computational overhead. Time-gated single-photon avalanche diod...
Image forgery localization is critical in defending against the malicious manipulation of image content, and is attracting increasing attention worldwide. In this paper, we propose a Dual-domain Fusion Swin Transformer U-Net (DFST-UNet) for image for...
Deep learning-based respiratory sound classification (RSC) has emerged as a promising non-invasive approach to assist clinical diagnosis. However, existing methods often face challenges, such as sub-optimal feature representation and limited model ex...
The detection of synthetic speech has become a pressing challenge due to the potential societal risks posed by synthetic speech technologies. Existing methods primarily focus on either the time or frequency domain of speech, limiting their ability to...
To address the technical limitations of traditional pavement performance prediction models in capturing temporal features and analyzing multi-factor coupling, this study proposes a Bayesian Optimization Dual-Layer Feature Fusion Model (BO-DLFF). The...
There are significant scale and morphological differences between fire and smoke features in forest fire detection. This paper proposes a detection method based on dual-branch multi-scale adaptive feature fusion network (DMAFNet). In this method, con...
The Dual-beam ion irradiation facility for Fusion materials (DiFU) has been developed and installed at the Ruđer Bošković Institute with the purpose to perform irradiation of samples of fusion materials by one or two ion beams. Ion b...
Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the...
Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubb...
In the transition to fully automated driving, the interaction between drivers and vehicles is crucial as drivers’ emotions directly influence their behavior, thereby impacting traffic safety. Currently, relying solely on a backbone based on a c...
Multi-Document Summarization (MDS) is a critical task in natural language processing that aims to condense document clusters into concise and comprehensive summaries. However, existing approaches based on large language models (LLMs) often lack struc...
Advancements in aviation technology have made intelligent navigation systems essential for improving flight safety and efficiency, particularly in low-visibility conditions. Radar and GPS systems face limitations in bad weather, making visible–...
Reference-based image super-resolution (RefSR) methods have achieved performance superior to that of single image super-resolution (SISR) methods by transferring texture details from an additional high-resolution (HR) reference image to the low-resol...
Bubble detection in water plays important roles in human exploration and management of the ocean. This research presents a detection technique based on laser polarization dual-mode fusion, aiming at solving the difficulties of light scattering intens...
Integrating infrared and visible-light images facilitates a more comprehensive understanding of scenes by amalgamating dual-sensor data derived from identical environments. Traditional CNN-based fusion techniques are predominantly confined to local f...
Convolutional neural networks have made significant progress in multimodal remote sensing image classification, but traditional convolutional neural networks are limited by fixed-size convolutional kernels, which are unable to effectively model and a...
Spine and hip abnormalities frequently occur together in most of the orthopedic population; therefore, both of these abnormalities impact the outcomes of the modalities that are being used. Few studies have reported reduced dislocation and revision r...
With the industrial demand caused by multi-sensor image fusion, infrared and visible image fusion (IVIF) technology is flourishing. In recent years, scale decomposition methods have led the trend for feature extraction. Such methods, however, have lo...
In multi-focus image fusion, accurately detecting and extracting focused regions remains a key challenge. Some existing methods suffer from misjudgment of focus areas, resulting in incorrect focus information or the unintended retention of blurred re...
Wind power forecasting is critical to grid stability and renewable energy integration. However, existing deep learning methods struggle to incorporate semantic domain knowledge from textual information, exhibit limited generalization with scarce trai...
In order to improve the recognition accuracy and model interpretability of metal fracture scanning electron microscope (SEM) images, this research presents an improved dual-branch model (IDBM) based on multi-scale feature fusion. This model employs V...
Automatic computer security inspection of X-ray scanned images has an irresistible trend in modern life. Aiming to address the inconvenience of recognizing small-sized prohibited item objects, and the potential class imbalance within multi-label obje...
Functional Movement Screening (FMS) is a movement pattern quality assessment system used to assess basic movement capabilities such as flexibility, stability, and pliability. Movement impairments and abnormal postures can be identified through peculi...
Deep neural networks provide a powerful driving force for breakthroughs in semantic segmentation technology. However, the current mainstream architecture generally falls into the “parameter redundancy trap” in pursuit of accuracy improvem...
To address the challenges of strong arc light noise, metal spatter, and smoke interference in weld seam recognition, we propose DRST-Net, a dual-branch cross-attention feature fusion network. The encoder of DRST-Net consists of two branches: ResNet50...
When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper p...
In the booming digital economy, data circulation—particularly for massive multimodal data generated by IoT sensor networks—faces critical challenges: ambiguous ownership and broken cross-domain traceability. Traditional property rights th...
Membrane fusion constitutes an essential step in the replication cycle of numerous viral pathogens, hence it represents an important druggable target. In the present study, we established a virus-free, stable reporter fusion inhibition assay (SRFIA)...
Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the abo...
The dual microstructure concept for gamma titanium aluminides (γ-TiAl) processed via electron beam–powder bed fusion (PBF-EB) provides a huge potential for more efficient jet turbine engines. While the concept is feasible and the mechanic...
This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of t...
This paper introduces a novel dual-stream collaborative architecture for remote sensing road segmentation, designed to overcome multi-scale feature conflicts, limited dynamic adaptability, and compromised topological integrity. Our network employs a...
In this paper, we propose an end-to-end low-light image enhancement network based on the YCbCr color space to address the issues encountered by existing algorithms when dealing with brightness distortion and noise in the RGB color space. Traditional...
The hermetic refrigeration compressor is the core component of the refrigeration system, failure of which will cause energy waste and reduce service life. Fault diagnosis based on vibration signal is a research hotspot. However, it is challenging to...
Low-light images commonly suffer from insufficient contrast, noise accumulation, and colour shifts, which impair human perception and subsequent visual tasks. We propose MambaDPF-Net—a dual-path fusion framework based on the retinal effect, adh...
Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast to optical imagery, there are several challenges in extracting benef...
Remote sensing image fusion can effectively solve the inherent contradiction between spatial resolution and spectral resolution of imaging systems. At present, the fusion methods of remote sensing images based on multi-scale transform usually set fus...
In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects...
Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capa...
In complex electromagnetic environments, radar systems face increasing challenges from advanced jamming techniques. These challenges mainly stem from the diversity of jamming patterns, the complexity of compound jamming signals, and the difficulty of...
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