Particle Swarm Optimization (PSO) has been extensively applied to optimization tasks in various domains, including image segmentation. In this work, we present a clustering-based segmentation algorithm that employs a multidimensional variant of PSO....
Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to...
A quick and effective way of segmenting images is the Otsu threshold method. However, the complexity of time grows exponentially as the number of thresolds rises. The aim of this study is to address the issues with the standard threshold image segmen...
Existing aerial forest fire monitoring data primarily consist of infrared or visible light images. However, there is a lack of in-depth research on the ability of models to perceive fire regions across different spectral images. To address this, we f...
Currently, there is no method available that can systematically score the available ecosystem services in streets or street segments in suburban districts. In this study, different climate adaptation measures and their ecosystem services were categor...
Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating...
A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available...
Object segmentation, a key type of image segmentation, focuses on detecting and delineating individual objects within an image, essential for applications like robotic vision and augmented reality. Despite advancements in deep learning improving obje...
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Tr...
Aiming at solving the problems of large calculation, time-consuming, and low segmentation accuracy of multi-threshold image segmentation, an adaptive threshold value based differential evolution algorithm is proposed in this paper. Firstly, an opposi...
The objective of few-shot segmentation is to segment novel categories given only a few annotated support images. Current FSS methods typically rely on pretrained backbone networks while often overlooking the inherent discrepancy between pretraining t...
Pulse-coupled neural networks (PCNNs) are capable of segmenting digital images in a multistage unsupervised fashion; however, optimal output selection remains challenging. To address the above problem, this paper emphasizes the role of the step size,...
Recent innovations in tissue clearing and light-sheet microscopy allow the rapid acquisition of intact micron-resolution images in fluorescently labeled samples. Automated, accurate, and high-throughput nuclear segmentation methods are in high demand...
Clustering analysis is one of the most important issues in trajectory data mining. Trajectory clustering can be widely applied in the detection of hotspots, mobile pattern analysis, urban transportation control, and hurricane prediction, etc. To obta...
This paper proposes a novel Piecewise Parabolic Approximate Computation method for hardware function evaluation, which mainly incorporates an error-flattened segmenter and an implementation quantizer. Under a required software maximum absolute error...
Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is o...
Superpixels could aggregate pixels with similar properties, thus reducing the number of image primitives for subsequent advanced computer vision tasks. Nevertheless, existing algorithms are not effective enough to tackle computing redundancy and inac...
Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei,...
Traditional DASH (dynamic adaptation streaming over HTTP(i.e., HyperText Transfer Protocol)) bitrate strategy cannot differentiate segments with different complexities of video content, resulting in the user’s QoE (quality of experience) of seg...
The particle size of pellets is an important parameter in steel big data, and the high density and high overlap rate of pellets bring a great challenge to particle size detection. To address this problem, a particle size intelligent detection algorit...
The rapid development of vision sensor based on artificial intelligence (AI) is reforming industries and making our world smarter. Among these trends, it is of great significance to adapt AI technologies into the intelligent agricultural management....
Sparse matrix-vector (SpMV) multiplication is a vital building block for numerous scientific and engineering applications. This paper proposes SURAA (translates to speed in arabic), a novel method for SpMV computations on graphics processing units (G...
Clamping is the key factor that restricts the full-process mechanization of directional shrimp peeling. To solve the problem of difficult clamping caused by the uncertainty of shrimp position after orientation, a segmented shrimp clamping method was...
Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements...
High precision geometric measurement of free-form surfaces has become the key to high-performance manufacturing in the manufacturing industry. By designing a reasonable sampling plan, the economic measurement of free-form surfaces can be realized. Th...
Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings. Large-scale remote sensing images often have multiple target d...
Due to the complex structure of high-canopy-density forests, the traditional individual tree segmentation (ITS) algorithms based on ALS point cloud, which set segmentation threshold manually, is difficult to adequately cover a variety of complex situ...
The application of computer vision and machine learning methods for semantic segmentation of the structural elements of 3D-printed products in the field of additive manufacturing (AM) can improve real-time failure analysis systems and potentially red...
Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds...
Automatic colorless floating hazardous and noxious substances (HNS) spill segmentation is an emerging research topic. Xylene is one of the priority HNSs since it poses a high risk of being involved in an HNS incident. This paper presents a novel algo...
Segmentation is one of the most important parts of medical image analysis. Manual segmentation is very cumbersome, time-consuming, and prone to inter-observer variability. Fully automatic segmentation approaches require a large amount of labeled trai...
Infrared and visible imaging play crucial roles in non-destructive testing, where accurate defect segmentation and detection are paramount. However, the scarcity of annotated training data or the limited number of data availability often poses a chal...
The paper presents a novel method for automated image processing that combines pyramidal generalized contour preprocessing with parallel–hierarchical segmentation, integrating adaptive multilevel thresholding to enhance segmentation accuracy an...
Navigation line extraction is critical for precision agriculture and automatic navigation. A novel method for extracting navigation lines based on machine vision is proposed herein using a straight line detected based on a high-ridge crop row. Aiming...
Object detection and segmentation have recently shown encouraging results toward image analysis and interpretation due to their promising applications in remote sensing image fusion field. Although numerous methods have been proposed, implementing ef...
Roadside LiDAR has become an important sensor for the detection of objects in cities, such as vehicles and pedestrians, which is due to its advantages of all-weather operation and high-ranging accuracy. In order to serve an intelligent transportation...
Surface cracks serve as early warning signals for potential geological hazards, and their precise segmentation is crucial for disaster risk assessment. Due to differences in acquisition conditions and the diversity of crack morphology, scale, and sur...
Roadside tree segmentation and parameter extraction play an essential role in completing the virtual simulation of road scenes. Point cloud data of roadside trees collected by LiDAR provide important data support for achieving assisted autonomous dri...
Optical coherence tomography angiography (OCTA) is an advanced non-invasive imaging technique that can generate three-dimensional images of retinal and choroidal vessels. It is of great value in the diagnosis and monitoring of a variety of ophthalmic...
Weakly supervised video anomaly detection (WS-VAD) is often addressed as a multi-instance learning problem in which a few fixed number of video segments are selected for classifier training. However, this kind of selection strategy usually leads to a...
The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which eas...
Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing th...
This manuscript introduces the first hp-adaptive mesh refinement algorithm for the Theory of Functional Connections (TFC) to solve hypersensitive two-point boundary-value problems (TPBVPs). The TFC is a mathematical framework that analytically satisf...
To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a...
Infrared small target detection (ISTD) plays a crucial role in many real-world applications. However, this task remains highly challenging due to the extremely small target size, low contrast, and complex background interference as infrared small tar...
Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to...
Addressing the challenges of high decoding latency, reduced spectral efficiency, and substantial storage requirements in a Cyclic Redundancy Check (CRC)Aided Successive Cancellation List (CA-SCL) polar decoder, this paper proposes a chaotic phase mod...
The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied...
Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutiona...
Gastrointestinal microbiota are essential for fiber degradation, energy metabolism, nutrient utilization, and environmental adaptation in ruminants. Although sika deer and reindeer both belong to the family Cervidae, they differ in ecology, feeding h...
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