This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, gr...
Hyperspectral remote sensing images, with their continuous, narrow, and rich spectra, hold distinct significance in the precise classification of land cover. Deep convolutional neural networks (CNNs) and their variants are increasingly utilized for h...
An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep le...
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, car...
Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and augmented reality, among others. In this paper, we propose a Multi-Scale Attentive Aggregation Network (MSAAN) to achieve the global consistency of point cloud...
In this study, the numerical investigation of the detectability of concrete aggregate segregation in circular concrete-filled steel tubulars (CCFST) based on piezoelectric lead zirconate titanate (PZT) measurement is performed. The stress wave propag...
Images captured on rainy days are prone to rain streaking on various scales. These images taken on a rainy day will be disturbed by rain streaks of varying degrees, resulting in degradation of image quality. This study sought to eliminate rain streak...
Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large propo...
Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure wi...
This research presents an advanced YOLOv8-MMD framework specifically designed for intelligent white radish harvesting systems, addressing the critical need for simultaneous species recognition and quality evaluation. The proposed architecture is buil...
Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent dat...
Accurate lesion segmentation in ultrasound images remains challenging because of severe speckle noise, low tissue contrast, ambiguous boundaries, and substantial variations in lesion morphology and scale. To address these limitations, we propose RDPA...
This study investigates how lithological origin influences the engineering performance, stripping resistance, and multi-scale material characteristics of aggregates used in asphalt pavement applications. Three representative aggregate systems—c...
Traditional convolution neural networks have achieved great success in human action recognition. However, it is challenging to establish effective associations between different human bone nodes to capture detailed information. In this paper, we prop...
Single image super-resolution (SISR) is a classical computer vision task that aims to reconstruct a high-resolution image from a low-resolution input, thereby improving detail sharpness and visual quality. In recent years, convolutional neural networ...
Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC1-1...
We propose an encoder–decoder architecture using wide and deep convolutional layers combined with different aggregation modules for the segmentation of medical images. Initially, we obtain a rich representation of features that span from low to...
The main challenges of semantic segmentation in vehicle-mounted scenes are object scale variation and trading off model accuracy and efficiency. Lightweight backbone networks for semantic segmentation usually extract single-scale features layer-by-la...
Cross-subject emotion recognition using EEG remains challenging due to substantial inter-individual variability. To address this, we propose a Multi-scale Spatio-Temporal Convolution and Multi-order Gated Spatial-Channel Aggregation Network (MCF-SCA)...
Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios...
Lane detection, as a basic environmental perception task, plays a significant role in the safety of automatic driving. Modern lane detection methods have obtained a better performance in most scenarios, but many are unsatisfactory in various scenario...
The key to image depth estimation is to accurately find corresponding points between the left and right images. A binocular camera can directly estimate the depth of the left and right image range, which completely avoids the requirement of target re...
Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The...
The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet met...
Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. The key lies in sufficiently exploiting both the specific long-...
Object detection (OD) in remote sensing (RS) images is an important task in the field of computer vision. OD techniques have achieved impressive advances in recent years. However, complex background interference, large-scale variations, and dense ins...
Accurate identification of the combustion state in municipal solid waste incineration (MSWI) processes is crucial for achieving efficient, low-emission, and safe operation. However, existing methods often struggle with stable and reliable recognition...
Models based on capsule neural network (CapsNet), a novel deep learning method, have recently made great achievements in hyperspectral remote sensing image (HSI) classification due to their excellent ability to implicitly model the spatial relationsh...
Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more com...
This paper proposes a multiplexing multi-scale features network (MMF-Network) for salient target detection to tackle the issue of incomplete detection structures when identifying salient targets across different scales. The network, based on encoder&...
Infertility is one of the most important health concerns worldwide. It is characterized by not being successful of pregnancy after some periods of periodic unprotected sexual intercourse. In vitro fertilization (IVF) is an assisted reproduction techn...
In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sp...
The multi-scale representation of remote sensing images provides various levels of image information crucial for decision-making in GIS applications and plays a significant role in information processing, data analysis, and geographic modeling. Tradi...
Underwater marine debris detection remains challenging because light attenuation, scattering, low contrast, blurred object boundaries, and complex seabed backgrounds degrade feature representation and hinder the reliable detection of small, sparsely...
Object detection in remote sensing scenarios faces multiple challenges, primarily manifested in multi-scale objects, class imbalance, densely packed small targets, and occlusion. To address these issues, we propose DCA-Net, an object detection method...
Accurate tumor segmentation in 3D medical images is essential for clinical diagnosis, treatment planning, and prognosis. However, this task remains challenging due to low contrast, irregular morphology, and indistinct boundaries. Most Segment Anythin...
As a core task in remote sensing image processing, change detection plays a vital role in dynamic surface monitoring for environmental management, urban planning, and agricultural supervision. However, existing methods often suffer from missed detect...
Accurate prediction of chiller energy consumption is crucial for the efficient operation and intelligent management of heating, ventilation, and air conditioning (HVAC) systems in large buildings. However, such prediction remains challenging due to t...
Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona sq...
Synthetic aperture radar images have all-weather and all-time capabilities and are widely used in the field of ship target surveillance at sea. However, its detection accuracy is often limited by factors such as complex sea conditions, diverse ship s...
Accurate segmentation of oral mucosal lesions remains challenging because lesion regions often resemble surrounding mucosa, with subtle color differences, weak texture contrast, unclear boundaries, and distractors such as saliva, folds, reflections,...
Recycled concrete, i.e., concrete which contains aggregates that are obtained from crushing waste concrete, typically exhibits a smaller strength than conventional concretes. We herein decipher the origin and quantify the extent of the strength reduc...
The extended finite element method (XFEM) is efficient in simulating crack initiation and its evolution process for reinforced-concrete (RC) structures due to its ability to solve fracture problems. Moreover, the multiscale numerical simulation helps...
The Qinghai–Tibet Plateau presents a unique challenge for infrastructure development due to its extreme geological and climatic conditions—high elevation, large diurnal temperature fluctuations, frequent freeze–thaw cycles, intense...
We study nonlinear stochastic dynamical systems that evolve in a Markov environment with separated fast and slow transition scales. These systems are also subject to impulsive perturbations under a Poisson approximation scheme. The environment is rep...
Visual place recognition (VPR) aims to localize a query image by matching its visual representation against a geotagged database. One major challenge in VPR is to learn place representations that remain robust under appearance changes, viewpoint vari...
As the only underground mural in the collection, the tomb murals are subject to damage due to temperature, humidity, and foundation settlement changes. Traditional mural inpainting takes a long time and requires experts to draw it manually. Therefore...
Semi-global matching (SGM) has been widely used in binocular vision. In spite of its good efficiency, SGM still has difficulties in dealing with low-texture regions. In this paper, an SGM algorithm based on multi-scale information fusion (MSIF), name...
To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm b...
Haze removal is a pre-processing step that operates on at-sensor radiance data prior to the physically based image correction step to enhance hazy imagery visually. Most current haze removal methods focus on point-to-point operations and utilize info...
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