Search Results - (( based segmentation using algorithm ) OR ( parameter selection based algorithm ))
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of classification result using OBIA is insufficient to depend on the segmentation parameters, the selection of features, and the existence of spectrally mixed objects. …”
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Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm
Published 2024“…Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. …”
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Extremal region selection for MSER detection in food recognition
Published 2021“…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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Mobile data gathering algorithms for wireless sensor networks
Published 2014“…The selection of this parameter is based on the application requirements. …”
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Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
Published 2022“…The algorithm will be able to identify the parameters, such as the number of pixels, area pixels, and mean value, by extracting the hippocampal shape. …”
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Extremal Region Selection for MSER Detection in Food Recognition
Published 2021“…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The second algorithm reduces the quantity of interest regions by using the Extremal Region Selection (ERS) algorithm. …”
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11
Edge detection of MRI images using artificial neural network
Published 2017“…Methods In this study, various existing edge detectors techniques based on spatial domain were evaluated. The best algorithm was selected subjectively, i.e. …”
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Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
Published 2016“…Two types of selection parameter namely tournament selection (TOS) and random permutation selection (RPS) are chosen. …”
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Interactive blood vessel segmentation from retinal fundus image based on canny edge detector
Published 2021“…After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). …”
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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16
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The elements extracted from the confusion matrix parameters (i.e. accuracy, specificity, sensitivity, AUC, precision and f-score) are used in benchmarking the optimal performance of classification algorithms. …”
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Real-time interpolator of CNC parametric curves with chord-error and feed-rateconstraints
Published 2025“…Teardrop 2-dimensional NURBS curve was selected based on varying features, shapes and dimensions. …”
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Development of new code for optical code division multiple access systems
Published 2011“…The simulations are carried out using various design parameters namely; distance, bit rate, input power and chip spacing. …”
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