Segmentation Scheme Research Articles

Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

Separating the dam body, spillway, and other structures from the point cloud in the dam area is an important step in dam deformation monitoring. Manual segmentation is time consuming and inaccurate. This study proposes a point cloud segmentation neural network model based on normal vector optimization suitable for dam environment: (1) This model utilizes the voxel uniform sampling method of equal length cubes to solve the problem of uneven point cloud density caused by wide range and long-distance measurement during point cloud measurement in dam areas. (2) Designed block input and combined output modules in the model, achieving efficient input of large volume point cloud and eliminating the impact of interpolation points offset during seq2seq model decoding process. (3) In response to the diverse characteristics of point cloud normal vectors presented by vegetation, rock mass, and complex dam structures in the dam area, this paper proposes an adaptive radius plane fitting vector estimation method based on eigenvalue method to improve the accuracy of segmentation. The experiment on the prototype arch dam shows that the proposed normal estimation method improves the classification accuracy of PointNet + + from 96.26 to 98.27%. Compared with the other three normal estimation methods (2-jets, Hough CNN, iterative PCA), the overall accuracy is improved by 0.82%, 1.22%, and 0.22%, and the joint average intersection is improved by 0.0293, 0.0325, and 0.0104. The prototype arch dam experiment shows that our proposed method has a segmentation accuracy of 98.27%. Compared with 2-jets, Hough CNN, and iterative PCA, the overall accuracy has been improved by 0.82%, 1.22%, and 0.22%. This study provides a high-precision segmentation scheme for applications such as deformation detection of dam components based on point clouds.

An Extended Method Based on the Geometric Position of Salient Image Features: Solving the Dataset Imbalance Problem in Greenhouse Tomato Growing Scenarios

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions.

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes.

A method of nucleus image segmentation and counting based on TC-UNet++ and distance watershed

Nucleus segmentation and counting play a crucial role in many cell analysis applications. However, the dense distribution and blurry boundaries of nucleus make nucleus segmentation tasks challenging. This paper proposes a novel segmentation and counting method. Firstly, TC-UNet++ is proposed to achieve a global segmentation. Then, the distance watershed method is used to finish local segmentation, which separate the adhesion and overlap part of the image. Finally, counting method is performed to obtain information on the counting number, area and center of mass of nucleus. TC-UNet++ achieved a Dice coefficient of 89.95% for cell instance segmentation on the Data Science Bowl dataset, surpassing the original U-Net++ by 0.23%. It also showed a 5.09% improvement in counting results compared to other methods. On the ALL-IDB dataset, TC-UNet++ reached a Dice coefficient of 83.97%, a 7.93% increase over the original U-Net++. Additionally, its counting results improved by 16.82% compared to other approaches. These results indicate that our method has a more complete and reasonable nucleus segmentation and counting scheme compared to other methods.

Retraction Note: Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation

Retraction Note: Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation

Tailoring customer segmentation strategies for luxury brands in the NFT market – The case of SUPERGUCCI

This study navigates the intersection of luxury fashion brands and Non-Fungible Tokens (NFTs) in the digital realm. By examining customer segments within the luxury NFT market, this study unfolds insights into changing market trends, consumer behaviors, and brand strategies. Employing data mining approaches, XPath analysis and the k-means clustering algorithm, this study examines user characteristics and behavior on the OpenSea platform, the largest NFT marketplace, with a specific focus on the SUPERGUCCI NFT collection by Gucci. This study identifies three distinct clusters, namely, speculators, casual collectors, and cryptocurrency natives, as well as their characteristics and behavior. The findings illustrate the significant impact of NFTs on customer segmentation and engagement, offering strategic insights for luxury brands navigating technological advancements. As the luxury sector evolves, this study enhances understanding of NFTs' role in consumer engagement and retail strategy within the digital age.

Customer Segmentation for Credit Card Customers : A Comprehensive Guide

This comprehensive article explores customer segmentation in the credit card industry, examining its theoretical foundations, methodologies, practical applications, and future trends. It discusses the evolution of segmentation techniques from traditional demographic-based approaches to advanced AI-driven models. The article highlights key segmentation criteria, data collection and preparation methods, and various segmentation techniques, including RFM analysis, clustering algorithms, and machine learning approaches. Case studies of successful implementations by American bankholding companies, multinational financial services corporations, and American bank-holding companies demonstrate the tangible benefits of effective segmentation strategies. The article also addresses common challenges in customer segmentation and provides recommendations for credit card companies to optimize their segmentation efforts. Finally, it explores future trends, including the increasing role of big data and AI in segmentation and the impact of evolving customer expectations on segmentation strategies.

A cascaded FAS-UNet+ framework with iterative optimization strategy for segmentation of organs at risk.

Segmentation of organs at risks (OARs) in the thorax plays a critical role in radiation therapy for lung and esophageal cancer. Although automatic segmentation of OARs has been extensively studied, it remains challenging due to the varying sizes and shapes of organs, as well as the low contrast between the target and background. This paper proposes a cascaded FAS-UNet+ framework, which integrates convolutional neural networks and nonlinear multi-grid theory to solve a modified Mumford-shah model for segmenting OARs. This framework is equipped with an enhanced iteration block, a coarse-to-fine multiscale architecture, an iterative optimization strategy, and a model ensemble technique. The enhanced iteration block aims to extract multiscale features, while the cascade module is used to refine coarse segmentation predictions. The iterative optimization strategy improves the network parameters to avoid unfavorable local minima. An efficient data augmentation method is also developed to train the network, which significantly improves its performance. During the prediction stage, a weighted ensemble technique combines predictions from multiple models to refine the final segmentation. The proposed cascaded FAS-UNet+ framework was evaluated on the SegTHOR dataset, and the results demonstrate significant improvements in Dice score and Hausdorff Distance (HD). The Dice scores were 95.22%, 95.68%, and HD values were 0.1024, and 0.1194 for the segmentations of the aorta and heart in the official unlabeled dataset, respectively. Our code and trained models are available at https://github.com/zhuhui100/C-FASUNet-plus .

TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation.

Concept drift arises from the uncertainty of data distribution over time and is common in data stream. While numerous methods have been developed to assist machine learning models in adapting to such changeable data, the problem of improperly keeping or discarding data samples remains. This may results in the loss of valuable knowledge that could be utilized in subsequent time points, ultimately affecting the model's accuracy. To address this issue, a novel method called time segmentation-based data stream learning method (TS-DM) is developed to help segment and learn the streaming data for concept drift adaptation. First, a chunk-based segmentation strategy is given to segment normal and drift chunks. Building upon this, a chunk-based evolving segmentation (CES) strategy is proposed to mine and segment the data chunk when both old and new concepts coexist. Furthermore, a warning level data segmentation process (CES-W) and a high-low-drift tradeoff handling process are developed to enhance the generalization and robustness. To evaluate the performance and efficiency of our proposed method, we conduct experiments on both synthetic and real-world datasets. By comparing the results with several state-of-the-art data stream learning methods, the experimental findings demonstrate the efficiency of the proposed method.

CoFiNet: Unveiling camouflaged objects with multi-scale finesse

Camouflaged Object Detection (COD) is a critical aspect of computer vision aimed at identifying concealed objects, with applications spanning military, industrial, medical and monitoring domains. To address the problem of poor detail segmentation effect, we introduce a novel method for camouflaged object detection, named CoFiNet. Our approach primarily focuses on multi-scale feature fusion and extraction, with special attention to the model’s segmentation effectiveness for detailed features, enhancing its ability to effectively detect camouflaged objects. CoFiNet adopts a coarse-to-fine strategy. A multi-scale feature integration module is laveraged to enhance the model’s capability of fusing context feature. A multi-activation selective kernel module is leveraged to grant the model the ability to autonomously alter its receptive field, enabling it to selectively choose an appropriate receptive field for camouflaged objects of different sizes. During mask generation, we employ the dual-mask strategy for image segmentation, separating the reconstruction of coarse and fine masks, which significantly enhances the model’s learning capacity for details. Comprehensive experiments were conducted on four different datasets, demonstrating that CoFiNet achieves state-of-the-art performance across all datasets. The experiment results of CoFiNet underscore its effectiveness in camouflaged object detection and highlight its potential in various practical application scenarios.

An automated phenotyping method for Chinese Cymbidium seedlings based on 3D point cloud

Aiming at the problems of low efficiency and high cost in determining the phenotypic parameters of Cymbidium seedlings by artificial approaches, this study proposed a fully automated measurement scheme for some phenotypic parameters based on point cloud. The key point or difficulty is to design a segmentation method for individual tillers according to the morphology-specific structure. After determining the branch points, two rounds of segmentation schemes were designed. The non-overlapping part of each tiller and the overlapping parts of each ramet are separated in the first round based on the edge point cloud-based segmentation, while in the second round, the overlapping part was sliced along the horizontal direction according to the weight ratio of the tillers above, to obtain the complete point cloud of all tillers. The core superiority of the algorithm is that the segmentation fits the tiller growth direction well, and the extracted skeleton points of tillers are close to the actual growth direction, significantly improving the prediction accuracy of the subsequent phenotypic parameters. Five phenotypic parameters, plant height, leaf number, leaf length, leaf width and leaf area, were automatically calculated. Through experiments, the accuracy of the five parameters reached 98.6%, 100%, 92.2%, 89.1%, and 82.3%, respectively, which reach the needs of various phenotypic applications.

Segmented catalyst layer with varied catalyst loading to improve the cost performance of proton exchange membrane electrolysis cell, a numerical investigation

In this work, a steady-state, three-dimensional and non-isothermal numerical model for a proton exchange membrane electrolysis cell (PEMEC) is established. Based on this model, the distribution of catalyst loading is specifically designed according to the flow characteristics of both parallel and serpentine channels. In general, the catalyst loading is progressively reduced from inlet to outlet along the flow direction to match the local water content, leading to an innovative segmented catalyst layer which has not been studied yet for PEMEC applications. The electrochemical behavior of different segmentation schemes is investigated by comparing their polarization curves normalized by either electrode area or catalyst loading, while other physical fields such as velocity, pressure, water mole fraction and temperature distributions are also characterized. The numerical results reveal that a reasonable distribution of catalyst can reduce its amount of usage while still maintaining most of the PEMEC performance. In other words, the cost performance is significantly improved. This is mainly attributed to the full use of uneven water distribution in the flow channel, which improves the catalyst utilization efficiency significantly. This strategy is proved to be effective for both serpentine and parallel channels, ensuring a broad application prospect.

A computer-vision based framework for virtual 3D garment reconstruction

AbstractExisting 3D garment reconstruction methods are difficult to implement for online fashion design and e-commerce or special applications. This paper proposes a novel computer-vision framework for 3D garment digital reconstruction, which aims to reconstruct high-quality and realistic virtual 3D garments with fabric mechanic properties for 3D virtual try-on. The new segmentation scheme is proposed to separate the 3D garment point clouds from background points, which is suitable for 3D human shapes and is adaptive for different 3D garment models in different scenes. The new Statistical Outlier Removal algorithm and the learning-based method PointCleanNet are combined to remove noise and outliers in 3D garment point clouds, which provides high-fidelity and high-quality 3D garment point clouds. The 3D garment meshes are then reconstructed from their corresponding point clouds with a modified rolling ball algorithm. Finally, the meshes are improved and converted into physics-based virtual try-on 3D garments with fabric mechanic properties added, which enables the assessment of different body shapes with varied sizes for the same reconstructed 3D garment. Comparison experiments demonstrate that our framework achieves high-quality and realistic 3D garment reconstruction and accurate 3D virtual try-on from 2D garment images. We also demonstrate the proposed framework on a large range of various garments to show this approach has a great potential for garment future technology, such as online garment shopping, garment design and manufacturing.

Enhanced Hybrid Algorithms for Segmentation and Reconstruction of Granular Grains From X‐Ray Micro Computed‐Tomography Images

ABSTRACTAccurate three‐dimensional (3D) reconstruction of granular grains from x‐ray micro‐computed tomography (µCT) images is a long‐standing challenge, particularly for dense soil samples. This study develops a machine learning (ML) enhanced approach to automatically reconstruct granular grains from µCT images. The novel academic contributions of this paper include (a) a hierarchical strategy based on parameter‐independent polygonal approximation, area, and concavity analysis, for the first time, to identify and eliminate both intergranular and intragranular voids; (b) incorporation of a recursive segmentation scheme and ML‐based grain classifier to avoid over‐segmentation; (c) novel modifications on the determination of splitting paths to enhance segmentation accuracy; and (d) an effective approach of assigning initial level set functions for reconstructing granular grains automatically. The hybrid ML algorithm is applied to µCT images of dense Mojave Mars Simulant. The results indicate that the proposed method can accurately segment grain clumps with unclear boundaries. The new automatic reconstruction algorithm eliminates ineffective operations and achieves a three‐fold increase in computational speed than previous methods documented in the literature. Ninety‐one percent of grains with distinct boundaries can be reconstructed and the reconstruction ratio reaches 81% even for grains without distinct boundaries. The overall reconstruction ratio of grains increases by 20% compared with previous methods, achieving a step‐change improvement for one‐to‐one mapping of real soil samples.

1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.

Modeling Realistic Geometries in Human Intrathoracic Airways.

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF

In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.

Model for increasing interest in enrolling in private universities based on a segmentation, targeting, and market positioning strategy in Indonesia

This study aims to examine the factors that influence interest in enrolling in private colleges in the DKI Jakarta area, with a focus on how the Segmentation, Targeting, and Positioning (STP) strategies of these colleges affect student enrollment decisions. The research addresses a gap in understanding how these marketing strategies impact enrollment in this specific region, which has not been widely studied. Using a survey method with a quantitative approach, the study explores aspects like accreditation, ranking, and clustering of colleges. Data were collected from 283 non-official and non-religious colleges, with a sample of 166 colleges, through questionnaires sent to policymakers. The data were analyzed using descriptive statistical methods and Structural Equation Modeling-Partial Least Squares (SEM-PLS) analysis. The results show that the segmentation strategy is particularly successful, while the targeting and positioning strategies are moderately effective. Unlike previous studies that generalize the effects of marketing strategies on college enrollment, this research offers a detailed analysis specific to the DKI Jakarta region. The hypothesis testing confirms that segmentation, targeting, and positioning strategies positively influence enrollment interest, both individually and together. However, the study is limited to one region and one type of college, indicating a need for further research in different contexts. The study concludes with recommendations to strengthen each part of the STP strategies by addressing identified weaknesses to improve their overall impact.

Multi-scale convolution enhanced transformer for multivariate long-term time series forecasting

In data analysis and forecasting, particularly for multivariate long-term time series, challenges persist. The Transformer model in deep learning methods has shown significant potential in time series forecasting. The Transformer model’s dot-product attention mechanism, however, due to its quadratic computational complexity, impairs training and forecasting efficiency. In addition, the Transformer architecture has limitations in modeling local features and dealing with multivariate cross-dimensional dependency relationship. In this article, a Multi-Scale Convolution Enhanced Transformer model (MSCformer) is proposed for multivariate long-term time series forecasting. As an alternative to modeling the time series in its entirety, a segmentation strategy is designed to convert the input original series into segmented forms with different lengths, then process time series segments using a new constructed multi-Dependency Aggregation module. This multi-Scale segmentation approach reduces the computational complexity of the attention mechanism part in subsequent models, and for each segment of length corresponds to a specific time scale, it also ensures that each segment retains the semantic information of the data sequence level, thereby comprehensively utilizing the multi-scale information of the data while more accurately capturing the real dependency of the time series. The Multi-Dependence Aggregate module captures both cross-temporal and cross-dimensional dependencies of multivariate long-term time series and compensates for local dependencies within the segments thereby captures local series features comprehensively and addressing the issue of insufficient information utilization. MSCformer synthesizes dependency information extracted from various temporal segments at different scales and reconstructs future series using linear layers. MSCformer exhibits higher forecasting accuracy, outperforming existing methods in multiple domains including energy, transportation, weather, electricity, disease and finance.

Real-Time Automatic M-Mode Echocardiography Measurement With Panel Attention.

Motion mode (M-mode) echocardiography is essential for measuring cardiac dimension and ejection fraction. However, the current diagnosis is time-consuming and suffers from diagnosis accuracy variance. This work resorts to building an automatic scheme through well-designed and well-trained deep learning to conquer the situation. That is, we proposed RAMEM, an automatic scheme of real-time M-mode echocardiography, which contributes three aspects to address the challenges: 1) provide MEIS, the first dataset of M-mode echocardiograms, to enable consistent results and support developing an automatic scheme; For detecting objects accurately in echocardiograms, it requires big receptive field for covering long-range diastole to systole cycle. However, the limited receptive field in the typical backbone of convolutional neural networks (CNN) and the losing information risk in non-local block (NL) equipped CNN risk the accuracy requirement. Therefore, we 2) propose panel attention embedding with updated UPANets V2, a convolutional backbone network, in a real-time instance segmentation (RIS) scheme for boosting big object detection performance; 3) introduce AMEM, an efficient algorithm of automatic M-mode echocardiography measurement, for automatic diagnosis; The experimental results show that RAMEM surpasses existing RIS schemes (CNNs with NL & Transformers as the backbone) in PASCAL 2012 SBD and human performances in MEIS.