Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods b...
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...
Multilevel threshold image segmentation is a key task in image processing, yet it faces challenges such as low search efficiency in high-dimensional spaces, difficulty in balancing segmentation accuracy and stability, and insufficient adaptability to...
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....
Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmenta...
A novel video object segmentation algorithm, which segments out multiple objects in a video sequence in unsupervised or weakly supervised manners, is proposed in this work. First, we match visually important object instances to construct salient obje...
In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation qua...
The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity’s shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on i...
Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datas...
To address the issues of uneven population initialization, insufficient individual information interaction, and passive boundary handling in the standard Golden Jackal Optimization (GJO) algorithm, while improving the accuracy and efficiency of multi...
Image segmentation is the process of dividing an image into homogeneous regions according to certain features and is widely used in image processing. Complexly structured images usually contain complex and essential objects. These images are non-line...
To address the limitations of the original Sand Cat Swarm Optimization (SCSO) algorithm—such as static strategy selection, insufficient population diversity, and coarse boundary handling—this paper proposes a multi-strategy enhanced versi...
To address the limitations of the original Secretary Bird Optimization Algorithm (SBOA), such as insufficient population diversity, weak local exploitation ability, and a tendency to become trapped in local optima when solving complex optimization pr...
To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs)...
As one of the ship energy efficiency optimization measures with the most energy saving and emission reduction potential, ship speed optimization has been recommended by the International Maritime Organization. In ship speed optimization, considering...
This paper presents a symmetry-guided multi-elite Gekko Japonicus Algorithm, termed MIGJA, for global optimization and multi-threshold image segmentation. The method modifies the original GJA from three aspects. In the movement stage, a success-rate...
Mountains are an important research object for surveying, mapping, cartography, space science, and ecological remote sensing. Automatic mountain segmentation is one of the most critical techniques in large-scale mountain analyses. However, several fa...
Forests play a vital role in increasing carbon sequestration in the biosphere. In recent years, segmenting forest canopy images in order to obtain various plant population parameters has become an essential means to assess the ecosystem. The objectiv...
We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of gr...
Multi-threshold segmentation of color images is a critical component of modern image processing. However, as the number of thresholds increases, traditional multi-threshold image segmentation methods face challenges such as low accuracy and slow conv...
To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fracti...
In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strateg...
Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence t...
Precise brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for clinical diagnosis and treatment planning. However, determining an optimal deep learning architecture for such tasks remains a challenge due to the vast hype...
In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and m...
To better address the issue of multi-threshold image segmentation, this paper proposes a hybrid adaptive crayfish optimization algorithm with differential evolution for color multi-threshold image segmentation (ACOADE). Due to the insufficient conver...
Forest canopy image is a necessary technical means to obtain canopy parameters, whereas image segmentation is an essential factor that affects the accurate extraction of canopy parameters. To address the limitations of forest canopy image mis-segment...
Multilevel thresholding image segmentation is one of the most widely used segmentation methods in the field of image segmentation. This paper proposes a multilevel thresholding image segmentation technique based on an improved whale optimization algo...
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...
To address the shortcomings of the information acquisition optimizer (IAO)—specifically its susceptibility to premature convergence, insufficient exploitation capability during later stages, and population diversity decay when applied to comple...
This study proposes a Hybrid Strategy Improved Dingo Optimization Algorithm (HSIDOA), designed to address the limitations of the standard DOA in complex optimization tasks, including its tendency to fall into local optima, slow convergence speed, and...
This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using g...
Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive p...
The purpose of this paper is to introduce a Particle Swarm Optimization (PSO) algorithm that eliminates the velocity vector while using only the personal best and global best positions to guide the search. An additional directional term is incorporat...
An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differ...
Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO)...
Image segmentation is the first step of image processing. It allows us to comprehend and extract information from the digital image. Multilevel thresholding is one of the most commonly used image segmentation techniques because of its simplicity and...
To classify Very-High-Resolution (VHR) imagery, Geographic Object Based Image Analysis (GEOBIA) is the most popular method used to produce high quality Land-Use/Land-Cover maps. A crucial step in GEOBIA is the appropriate parametrization of the segme...
An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluatio...
A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algo...
This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aeri...
In object-based image analysis (OBIA), the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimizat...
The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep s...
Remote sensing-based Earth observation provides essential spatial data for analyzing and monitoring both natural and urban environments. Precise characterization of objects in these scenes is vital for environmental management, land-use planning, and...
Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective u...
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lea...
In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of...
The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmenta...
One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper propos...
Color multi-threshold image segmentation is a non-convex, gradient-free global optimization problem. The number of decision variables increases with the number of thresholds, leading to a rapid expansion of the search space and increased computationa...
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