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Masked Topology Convolutional Network for classification and segmentation of Remote Sensing Images

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 18-18页

作者： Wang, Falin Ji, Jian Wang, Yuan Li, Jingyang Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Convolutional neural networks have made significant progress in remote sensing image processing. Convolutional networks mostly model the local information of samples based on pixel features, while ignoring the topological relationship among different categories of ground objects. However, the nonlocal topology can better represent the underlying data structure of the image. In order to make up for the insufficiency of Convolutional neural networks in extracting nonlocal topological information, we propose a new masked topology convolutional network (MTC_Net). First, considering the regionality of different categories of ground objects, we use a clustering algorithm to simply cluster the pixels to extract the most obvious area mask, which can strengthen the boundary information among different ground objects. Second, we take the cluster center point as the representative point of the region and use the graph structure to represent the topological relationship, in addition use graph convolution to further extract the topological structure relationship among different categories of ground objects. Finally, we use convolution modules and multilevel feature attention modules to capture important local and global contextual semantic information and reduce category confusion. We conduct a great deal of experiments on four (two classification and two segmentation) public datasets, and the proposed MTC_Net achieves excellent classification and segmentation performance.

关键词： Feature extraction Convolutional neural networks Data mining Image segmentation Topology Clustering algorithms Network topology classification and segmentation graph convolutional network (GCN) remote sensing images topological structure

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Adaptive Multiview Graph Convolutional Network for 3-D Point Cloud classification and segmentation

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第6期16卷 2043-2054页

作者： Niu, Wanhao Wang, Haowen Zhuang, Chungang Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

Point cloud classification and segmentation are crucial tasks for point cloud processing and have wide range of applications, such as autonomous driving and robot grasping. Some pioneering methods, including PointNet, VoxNet, and DGCNN, have made substantial advancements. However, most of these methods overlook the geometric relationships between points at large distances from different perspectives within the point cloud. This oversight constrains feature extraction capabilities and consequently limits any further improvements in classification and segmentation accuracy. To address this issue, we propose an adaptive multiview graph convolutional network (AM-GCN), which comprehensively synthesizes both the global geometric features of the point cloud and the local features within the projection planes of multiple views through an adaptive graph construction method. First, an adaptive rotation module in AM-GCN is proposed to predict a more favorable angle of view for projection. Then, a multilevel feature extraction network can flexibly be constructed by spatial-based or spectral-based graph convolution layers. Finally, AM-GCN is evaluated on ModelNet40 for classification, ShapeNetPart for part segmentation, ScanNetv2 and S3DIS for scene segmentation, which demonstrates the robustness of the AM-GCN with competitive performance compared with existing methods. It is worth noting that it performs state-of-the-art performance in many categories.

关键词： Point cloud compression Three-dimensional displays Convolution Feature extraction Adaptive systems Task analysis Semantics Adaptive rotation classification and segmentation graph convolutional network (GCN) multiview point cloud

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Curvature-Variation-Inspired Sampling for Point Cloud classification and segmentation

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1868-1872页

作者： Zhu, Lei Chen, Weinan Lin, Xubin He, Li Guan, Yisheng Guangdong Univ Technol Biomimet & Intelligent Robot Lab BIRL Guangzhou 510006 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China

Point cloud is a discrete and unordered expression of 3D data. A lot of methods have been proposed to solve the problem in 3D object classification and scene recognition. To handle the huge amount of unordered point cloud, down-sampling before processing is needed. The shortage of existing sampling methods is the lack of geometry information consideration, which is essential for point cloud classification and segmentation tasks. Our method is mainly motivated by the observation that points with a high curvature variation can depict the outlines of objects. Thus, we propose a curvature variation based sampling method for point cloud classification and segmentation tasks. We aim to sample points with high curvature variations, which are considered to be more suitable for classification and segmentation tasks than the traditional sampling method. We combine the proposed sampling algorithm with the existing sampling method for multiple information fusion, and a higher accuracy and mean IoU can be achieved. The experimental results verify the advantage of considering curvature variation in classification and segmentation tasks.

关键词： Point cloud compression Shape Task analysis Geometry Three-dimensional displays Sampling methods Convolution Curvature variation classification and segmentation point cloud

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A Modified PointNet-Based DDoS Attack classification and segmentation in Blockchain

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Computer Systems Science & Engineering 2023年第10期47卷 975-992页

作者： Jieren Cheng Xiulai Li Xinbing Xu Xiangyan Tang Victor S.Sheng School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China Hainan Hairui Zhong Chuang Technol.Co.Ltd. Haikou570228China Department of Computer Science Texas Tech University TX 79409USA

With the rapid development of blockchain technology,the number of distributed applications continues to increase,so ensuring the security of the network has become particularly ***,due to its decentralized,decentralized nature,blockchain networks are vulnerable to distributed denial-of-service(DDoS)attacks,which can lead to service stops,causing serious economic losses and social *** research questions in this paper mainly include two aspects:first,the classification of DDoS,which refers to detecting whether blockchain nodes are suffering DDoS attacks,that is,detecting the data of nodes in parallel;The second is the problem of DDoS segmentation,that is,multiple pieces of data that appear at the same time are determined which type of DDoS attack they belong *** order to solve these problems,this paper proposes a modified PointNet(MPointNet)for the classification and type segmentation of DDoS attacks.A dataset containing multiple DDoS attack types was constructed using the CIC-DDoS2019 dataset,and trained,validated,and tested *** results show that the proposed DDoS attack classification method has high performance and can be used for the actual blockchain security maintenance *** accuracy rate of classification tasks reached 99.65%,and the accuracy of type segmentation tasks reached 85.47%.Therefore,the method proposed in this paper has high application value in detecting the classification and segmentation of DDoS attacks.

关键词： Blockchain DDoS PointNet classification and segmentation

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A novel multitask transformer deep learning architecture for joint classification and segmentation of horticulture plantations using very High-Resolution satellite imagery

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 227卷

作者： Vinod, P. V. Behera, M. D. Prakash, A. Jaya Hebbar, R. Srivastav, S. K. Reg Remote Sensing Ctr RRSC NRSC Bangalore India Indian Inst Technol Kharagpur Ctr Ocean River Atmosphere & Land Sci Kharagpur West Bengal India ISRO Reg Ctr NRSC New Delhi India

This study introduces MultiTaskDeiTUNet, a novel multitask deep learning architecture designed to tackle the dual challenges of classifying tree plantation densities and segmenting tree crowns in high-resolution (0.7 m) satellite imagery. The core challenge lies in the overlapping spatial patterns of various tree species and densities, complicating the accurate extraction and classification of individual tree crowns. MultiTaskDeiTUNet integrates the Data-efficient Image Transformer (DeiT) with the U-Net model, harnessing DeiT's strength in contextual detail recognition and spatial dependency capture for density classification, alongside U-Net's proficiency in capturing low-level features for precise crown segmentation. By addressing the complexities of high-resolution data handling and the simultaneous execution of classification and segmentation tasks, MultiTaskDeiTUNet achieves an average F1 score of 0.91 (+ 0.03) and a precise tree crown segmentation with mIoU of 0.73 (+ 0.01). The DeiT backbone adeptly learns shared features such as canopy shapes and spatial arrangements, which are crucial for both tasks and enhance overall model performance. Ablation studies underscore the specialized roles of each component: freezing DeiT's weights results in reduced classification accuracy with an average F1 score of 0.48 (+ 0.08), while freezing U-Net's weights yields a reduced mIoU of 0.29 (+ 0.12) This differentiation highlights DeiT's excellence in classification tasks and U-Net's superiority in segmentation. Substituting the DeiT model with a standard ViT model further highlights the effectiveness of DeiT, as the ViT model demonstrated lower accuracy, with an average F1 score of 0.87 (+0.05) compared to DeiT's F1 score of 0.91 (+0.03). Statistical analysis revealed right-skewed distributions in tree crown areas across density categories. The efficacy of MultiTaskDeiTUNet in tree plantation analysis indicates its potential applicability to a wide range of horticultural pl

关键词： DeiT U -Net Transformer Joint loss Intersection of Union (IoU) classification and segmentation

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Multi-Head Attentional Point Cloud classification and segmentation Using Strictly Rotation-Invariant Representations

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IEEE ACCESS 2021年 9卷 71133-71144页

作者： Tao, Zhiyong Zhu, Yixin Wei, Tong Lin, Sen Liaoning Tech Univ Sch Elect & Informat Engn Huludao 125105 Peoples R China Eotvos Lorand Univ Fac Informat H-1117 Budapest Hungary Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang 110159 Peoples R China

Point cloud processing plays an increasingly essential role in three-dimensional (3D) computer vision target, scene parsing, environmental perception, etc. Compared with using aligned point cloud data for classification and segmentation, the strictly rotation-invariant representations show enough robustness. Inspired by the great success of deep learning, we propose a novel neural network called Multi-head Attentional Point Cloud classification and segmentation Using Strictly Rotation-invariant Representations. Our research focuses on processing the point cloud rotated in any direction effectively and precisely. First of all, the strictly rotation-invariant point cloud representations are obtained through point projection. Then we apply a multi-head attentional convolution layer (MACL) using attention coding to develop the performance of point cloud feature extraction. Finally, our network assigns different responses and recognizes the overall geometry well through a key point descriptor, adding to the global feature. Our method can explore more in-depth information for accuracy enhancement with attention pooling and multi-layer perceptron (MLP) based on an advanced DenseNet. Our network enjoys 90.63% and 87.50% classification accuracy testing on ModelNet10 and ModelNet40, and 75.15% intersection over union metric (mIoU) evaluating on ShapeNet Part dataset, remaining under any rotation. Rotating experimental results indicate that our framework realizes better point cloud classification and segmentation performance than most state-of-the-art methods.

关键词： Three-dimensional displays Convolution Robustness Neural networks Feature extraction Deep learning Data processing Point cloud deep learning strictly rotation-invariant representations attention coding classification and segmentation

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Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 149卷 105941-105941页

作者： Yalcin, Sercan Vural, Huseyin Adiyaman Univ Dept Comp Engn TR-02040 Adiyaman Turkey

Accurate diagnosis of brain stroke, classification and segmentation of the stroke are extremely important for physicians to focus on specific points of the brain and apply the right treatment to patients. Encoder-decoder deep learning-based methods have been effectively integrated into many artificial intelligence applications. On the other hand, such networks have many disadvantages due to sampling methods, learning methodologies, and efficient operations. In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke. A convolutional deep network architecture is proposed with an optimized dimensional U-Net (D-UNet) by blocking and adaptively sequencing the convolution layers and then optimizing the number of activation functions and hyperparameters. The proposed method examines the computed tomography (CT) images from the dataset used to determine whether there is a brain stroke. It can determine if a stroke is caused by ischemia or hemorrhage once it has occurred. Additionally, the proposed method can precisely reveal the region overlaid by the radiologist and segment the existing stroke. The proposed method is compared with other existing CNN-type architectures by performing various experiments on the same real dataset via Python scripts. The results show that the proposed model performs well, with accuracy rates for stroke classification of 98.9% and ischemia and hemorrhage classification of 98.5%, respectively. Moreover, the segmentation of brain strokes using the proposed model yielded an intersection over union (IoU) rate of 95.2%.

关键词： Brain stroke detection Convolutional neural networks Ischemia and hemorrhage stroke classification and segmentation

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Advancements in deep learning for point cloud classification and segmentation: A comprehensive review

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Computers & Graphics 2025年 130卷

作者： Wei Zhou Kunlong Liu Weiwei Jin Qian Wang Yunfeng She Yongxiang Yu Caiwen Ma School of Information Science and Technology Northwest University China Department of Electrical and Automatic Engineering East China Jiaotong University China Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences China

Point clouds, a foundational 3D data representation, are extensively utilized in fields such as autonomous driving and robotics due to their capability to represent complex spatial structures. With the rapid advancement of artificial intelligence, leveraging deep learning to enhance point cloud processing has become a central focus in computer vision research. The unstructured nature, large-scale data volume, and labor-intensive annotation of point clouds present unique challenges for designing deep learning models. This paper provides a comprehensive review of the development and latest advancements in deep learning models for point cloud processing, with a specific focus on classification and segmentation. We systematically outline the technical approaches and key strategies for addressing these challenges, offering a clear understanding of the most recent and notable research in the field. Furthermore, we discuss the potential challenges and future research directions in point cloud processing by analyzing the respective strengths and weaknesses of prevailing techniques, thus to guide the evolution of point cloud processing technologies.

关键词： 3D point cloud understanding Supervised learning Unsupervised learning classification and segmentation Mamba

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Continuous Motion classification and segmentation Based on Improved Dynamic Time Warping Algorithm

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年第2期32卷 1850002-1850002页

作者： Liu, Mingqin Zhang, Xiaoguang Xu, Guiyun China Univ Min & Technol Coll Mech & Elect Engn Xuzhou Peoples R China Huaihai Inst Technol Coll Mech Engn Lianyungang Peoples R China

The continuous image sequence recognition is more difficult than the single image recognition because the classification of continuous image sequences and the image edge recognition must be very accurate. Hence, a method based on sequence alignment for action segmentation and classification is proposed to reconstruct a template sequence by estimating the mean action of a class category, which calculates the distance between a single image and a template sequence by sparse coding in Dynamic Time Warping. The proposed method, the methods of Kulkarni et al. [Continuous action recognition based on sequence alignment, Int. J. Comput. Vis. pp. 1-26.] and Hoai et al. [Joint segmentation and classification of human actions in video, IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 108-119.] are compared on the recognition accuracy of the continuous recognition and isolated recognition, which clearly shows that the proposed method outperforms the other methods. When applied to continuous gesture classification, it not only can recognize the gesture categories more quickly and accurately, but is more realistic in solving continuous action recognition problems in a video than the other existing methods.

关键词： classification and segmentation continuous action sequence alignment dynamic time warping image processing

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Multi-task interaction learning for accurate segmentation and classification of breast tumors in ultrasound images

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PHYSICS IN MEDICINE AND BIOLOGY 2025年第6期70卷 065006-065006页

作者： Zheng, Shenhai Li, Jianfei Qiao, Lihong Gao, Xi Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China

Objective. In breast diagnostic imaging, the morphological variability of breast tumors and the inherent ambiguity of ultrasound images pose significant challenges. Moreover, multi-task computer-aided diagnosis systems in breast imaging may overlook inherent relationships between pixel-wise segmentation and categorical classification tasks. Approach. In this paper, we propose a multi-task learning network with deep inter-task interactions that exploits the inherently relations between two tasks. First, we fuse self-task attention and cross-task attention mechanisms to explore the two types of interaction information, location and semantic, between tasks. In addition, a feature aggregation block is developed based on the channel attention mechanism, which reduces the semantic differences between the decoder and the encoder. To exploit inter-task further, our network uses an circle training strategy to refine heterogeneous feature with the help of segmentation maps obtained from previous training. Main results. The experimental results show that our method achieved excellent performance on the BUSI and BUS-B datasets, with DSCs of 81.95% and 86.41% for segmentation tasks, and F1 scores of 82.13% and 69.01% for classification tasks, respectively. Significance. The proposed multi-task interaction learning not only enhances the performance of all tasks related to breast tumor segmentation and classification but also promotes research in multi-task learning, providing further insights for clinical applications.

关键词： breast ultrasound deep learning multi-task attention mechanism classification and segmentation

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