The datasets in the latest semantic segmentation model often need to be manually labeled for each pixel, which is time-consuming and requires much effort. General models are unable to make better predictions, for new categories of information that ha...
To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weig...
Tooth segmentation is an important aspect of virtual orthodontic systems. In some existing studies using deep learning-based tooth segmentation methods, the feature learning of point coordinate information and normal vector information is not effecti...
Interactive segmentation is a technique for picking objects of interest in images according to users’ input interactions. Some recent works take the users’ interactive input to guide the deep neural network training, where the users&rsquo...
Semantic segmentation is one of the most active research topics in computer vision with the goal to assign dense semantic labels for all pixels in a given image. In this paper, we introduce HFEN (Hierarchical Feature Extraction Network), a lightweigh...
Marine animal segmentation aims at segmenting marine animals in complex ocean scenes, which plays an important role in underwater intelligence research. Due to the complexity of underwater scenes, relying solely on a single RGB image or learning from...
Panoptic segmentation, a pivotal research direction in computer vision, unifies pixel-level object and background recognition within a scene, crucial for applications like autonomous driving. However, existing methods, including State-of-the-Art mode...
Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomp...
As a popular research subject in the field of computer vision, knowledge distillation (KD) is widely used in semantic segmentation (SS). However, based on the learning paradigm of the teacher–student model, the poor quality of teacher network f...
In the final approach stage of rendezvous and docking of a spacecraft, the pose parameters of the target spacecraft need to meet docking or berthing capture conditions. Visible light visual measurement systems are increasingly employed in spacecraft...
Medical image segmentation acts as a foundational element of medical image analysis. Yet its accuracy is frequently limited by the scale fluctuations of anatomical targets and the intricate contextual traits inherent in medical images—including...
Timely detection of defects is essential for ensuring safe and stable operation of concrete buildings. Automatic segmentation of concrete buildings’ surfaces is challenging due to the high diversity of crack appearance, the detailed information...
This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which i...
Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. Image segmentation is one of the most used techniques for medical support t...
In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study pr...
Visual foundation models exhibit significant potential for extracting generalized characteristics from imagery, which is crucial for remote sensing image interpretation tasks. However, current approaches underutilize these capabilities for hyperspect...
Semantic segmentation plays a critical role in medical image analysis, offering indispensable information for the diagnosis and treatment planning of liver diseases. However, due to the complex anatomical structure of the liver and significant inter-...
Extracting buildings and water bodies from high-resolution remote sensing images is of great significance for urban development planning. However, when studying buildings and water bodies through high-resolution remote sensing images, water bodies ar...
Standard SAM-based approaches in medical imaging typically rely on explicit geometric prompts, such as bounding boxes or points. However, these rigid spatial constraints are often insufficient for capturing the complex, deformable boundaries of medic...
The recently proposed multi-objective clustering methods convert the segmentation problem to a multi-objective optimization problem by extracting multiple features from an image to be segmented as clustering data. However, most of these methods fail...
To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of dee...
Multi-organ medical image segmentation is essential for accurate clinical diagnosis, effective treatment planning, and reliable prognosis, yet it remains challenging due to complex backgrounds, irrelevant noise, unclear organ boundaries, and wide var...
Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhance...
The complex structures and diverse object categories in indoor environments pose significant challenges for point cloud semantic segmentation. To address the insufficient capability of extracting local features in complex scenes, this paper proposes...
Recently, deep learning methods have widely been employed for surface defect segmentation in industrial production with remarkable success. Nevertheless, accurate segmentation of various types of defects is still challenging due to their irregular ap...
Many major diseases can cause changes in the morphology of blood vessels, and the segmentation of retinal blood vessels is of great significance for preventing these diseases. Obtaining complete, continuous, and high-resolution segmentation results i...
Vessel segmentation in fundus images is crucial for diagnosing eye diseases. The rapid development of deep learning has greatly improved segmentation accuracy. However, the scale of the retinal blood-vessel structure varies greatly, and there is a lo...
High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications and it also has military and civil applications. The scene analysis needs to be automated as much as possible with high-resolution r...
Camouflaged target segmentation has been widely used in both civil and military applications, such as wildlife behaviour monitoring, crop pest control, and battle reconnaissance. However, it is difficult to distinguish camouflaged objects and natural...
In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based o...
The semantic segmentation of small objects in point clouds is currently one of the most demanding tasks in photogrammetry and remote sensing applications. Multi-resolution feature extraction and fusion can significantly enhance the ability of object...
Linear feature extraction is crucial for special objects in semantic segmentation networks, such as slot marking and lanes. The objects with linear characteristics have global contextual information dependency. It is very difficult to capture the com...
Facial skin analysis has attracted considerable attention in the skin health domain. The results of facial skin analysis can be used to provide skin care and cosmetic recommendations in aesthetic dermatology. Because of the existence of several skin...
Background/Objectives: Mesothelioma is a rare and aggressive form of cancer that primarily affects the lining of the lungs, abdomen, or heart. It typically arises from exposure to asbestos and is often diagnosed at advanced stages. Limited datasets a...
Semantic segmentation of remote sensing images plays a crucial role in accurate land-cover classification and environmental monitoring. However, existing semantic segmentation networks still struggle with multiscale feature extraction and context mod...
Colorectal cancer is a common malignant tumor. Colorectal cancer is primarily caused by the cancerization of an adenomatous polyp. Segmentation of polyps in computer-assisted enteroscopy images is helpful for doctors to diagnose and treat the disease...
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex sha...
Image–text multimodal deep semantic segmentation leverages the fusion and alignment of image and text information and provides more prior knowledge for segmentation tasks. It is worth exploring image–text multimodal semantic segmentation...
Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net...
Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentat...
Among the existing insulator defect detection methods, the automatic detection of inspection robots based on the instance segmentation algorithm is relatively more efficient, but the problem of the limited accuracy of the segmentation algorithm is st...
Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for cla...
Understanding remote sensing imagery is vital for object observation and planning. However, the acquisition of optical images is inevitably affected by shadows and occlusions, resulting in local discrepancies in object representation. To address thes...
Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term...
The segmentation of the navigation area from water images is of great significance in the safe and automated navigation of unmanned vessels. However, accurate segmentation of water boundaries in real time under the interference of water-surface light...
Urban image semantic segmentation faces challenges including the coexistence of multi-scale objects, blurred semantic relationships between complex structures, and dynamic occlusion interference. Existing methods often struggle to balance global cont...
With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the l...
Chromosome karyotype analysis is of great clinical importance in the diagnosis and treatment of diseases. Since manual analysis is highly time and effort consuming, computer-assisted automatic chromosome karyotype analysis based on images is routinel...
Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR. However, the...
Existing point cloud semantic segmentation approaches do not perform well on details, especially for the boundary regions. However, supervised-learning-based methods depend on costly artificial annotations for performance improvement. In this paper,...
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