检索结果-内蒙古大学图书馆

Results in Engineering 2025年 26卷

作者： Tamilarasan, A. Rajamani, D. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Centre for Advanced Materials Processing Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai600062 India

image segmentation is a critical aspect of image processing, particularly for applications requiring precise object identification. This study introduces a novel multilevel thresholding technique for grayscale image segmentation based on the Snake Optimizer (SO) algorithm, which is inspired by the mating behaviour of snakes. The proposed method aims to maximize fuzzy entropy to determine optimal threshold values, thereby enhancing segmentation accuracy. Extensive experiments were conducted on ten benchmark images, utilizing 2 to 5 thresholds, and comparing the performance of the SO algorithm against three state-of-the-art algorithms: Salp Swarm Algorithm (SSA), Grey Wolf Optimization (GWO), and Moth Flame Optimization (MFO).The results demonstrate that the SO algorithm consistently outperforms its counterparts across various metrics, achieving the highest average fitness values in 35 out of 40 attempts, with notable improvements in PSNR, SSIM, and FSIM. Specifically, the SO achieved an average PSNR of 26.617 for the 'Barbara' image at 5 thresholds, surpassing SSA (26.568), GWO (26.561), and MFO (26.515). Additionally, the SO recorded an SSIM of 0.9575 for the same image, significantly higher than SSA (0.9339), GWO (0.9114), and MFO (0.8934). The computational efficiency of the SO is also highlighted, with lower average CPU times, achieving 3.1217 s for the 'Ostrich' image at 2 thresholds, compared to SSA (3.1298), GWO (3.8539), and MFO (3.9343).Statistical analysis using Wilcoxon rank-sum tests further confirms the significant performance advantages of the SO algorithm, establishing it as a robust and effective solution for multilevel thresholding in image segmentation tasks. © 2025 The Author(s)

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Fast Full-frame Video Stabilization with Iterative Optimization

Fast Full-frame Video Stabilization with Iterative Optimizat...

引用

International Conference on computer Vision (ICCV)

作者： Weiyue Zhao Xin Li Zhan Peng Xianrui Luo Xinyi Ye Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Albany Albany NY

Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multi-frame fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GLAND SEGMENTATION VIA DUAL ENCODERS AND BOUNDARY-ENHANCED ATTENTION

arXiv

引用

arXiv 2024年

作者： Wang, Huadeng Yu, Jiejiang Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Pathology Ganzhou Municipal Hospital Ganzhou China

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensing imagery using a Cross Semantic Attribute-guided Transformer

RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensin...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Damalla Rambabu Swetha G Rajeshreddy Datla Vishnu Chalavadi C Krishna Mohan Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Dept. of Space Advanced Data Processing Research Institute (ADRIN) Secunderabad India Indian Institute of Technology Tirupati India

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot learning (ZSL) based scene classification aims to recognize unseen classes by transferring semantic information from seen classes. The applicability of ZSL for scene classification in remote sensing images becomes challenging due to the complexity of scenes. Earlier attention-based methods are ineffective for extracting discriminative region-based features within a single image. This limitation hinders their ability to achieve transferability and accurately localize object attributes, which is essential for extracting discriminative region-based features. Hence, we propose a method for zero-shot scene classification in remote sensing images using a cross-semantic attribute-guided Transformer named RSZero-CSAT. Firstly, the semantic information is acquired using shared remote sensing semantic attributes to localize object attributes that characterize discriminative region features. Then, a Transformer in RSZero-CSAT is employed to localize object attributes within visual features accurately, enhancing the effectiveness of semantic information transfer in ZSL. Specifically, the RSZero-CSAT employs a semantic attribute → visual Transformer (SAVT) and a visual → semantic attribute Transformer (VSAT) components to extract visual features guided by semantic attributes and semantic attribute features guided by visual features, respectively. Further, SAVT and VSAT mutually learn and collaborate to obtain semantically enriched visual representations, leveraging prediction-level and feature-level semantic collaborative losses for capturing crucial semantic information. Finally, the semantically enriched visual representations obtained from SAVT and VSAT are combined to facilitate visual-semantic interactions in collaboration with class semantic vectors to classify ZSL. Our experimental results demonstrate the impact of RSZero-CSAT in improving the performance of unseen classes on four scene classification benchmark datasets in remote sensing images. The code is avai

关键词： Visualization Scene classification Zero-shot learning Semantics Collaboration Benchmark testing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in Machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： computer vision Accuracy Perturbation methods computer network reliability Machine learning Robustness Security Medical diagnosis Resilience image classification

来源：评论

学校读者我要写书评

暂无评论

Visibility-Aware Human Mesh Recovery via Balancing Dense Correspondence and Probability Model

Visibility-Aware Human Mesh Recovery via Balancing Dense Cor...

引用

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Yanjun Wang Wenjia Wang Jun Ling Rong Xie Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science The University of Hong Kong Hong Kong China

ISBN: (数字)9798350379815

ISBN: (纸本)9798350379822

Reconstructing the human body mesh often faces challenges like self-occlusion, object occlusion, and interference from other people. However, focusing on the model's robustness in scenarios of occlusion leads to a compromise in the accuracy of estimating non-occluded humans. Striking the right balance is a research question worth exploring. In this study, we introduce the Visibility-aware Human Mesh Recovery (VisHMR) method, which dynamically adapts to different scenarios based on the visibility of different body parts. It enhances robustness in estimating occluded areas of the human body, while also accurately regressing the visible parts. Specifically, VisHMR introduces a dual-branch network that models and estimates occluded and non-occluded regions separately. In visible regions, we establish dense correspondence between the image and rotation in UV space. In occluded regions, we utilize a Normalizing Flow model to hypothesize the most probable poses. Besides, a fusion module is designed to integrate the results of the dual-branch network, yielding a precise yet robust rotation parameter. Extensive experiments reveal that VisHMR significantly outperforms previous state-of-the-art methods in dealing with occlusions, and performs comparably to the best methods on standard non-occluded benchmarks.

关键词： Solid modeling Adaptation models Accuracy Three-dimensional displays Shape Interference Predictive models

来源：评论

学校读者我要写书评

暂无评论

Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection

arXiv

引用

arXiv 2023年

作者： Wu, Yunan Castro-Macías, Francisco M. Morales-Álvarez, Pablo Molina, Rafael Katsaggelos, Aggelos K. Image and Video Processing Laboratory Department of Electrical and Computer Engineering Northwestern University United States Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Statistics and Operations Research University of Granada Spain University of Granada Spain

Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assumes that instances in each bag are independent samples from a given distribution. However, instances are often spatially or sequentially ordered, and one would expect similar diagnostic importance for neighboring instances. To address this, in this study, we propose a smooth attention deep MIL (SA-DMIL) model. Smoothness is achieved by the introduction of first and second order constraints on the latent function encoding the attention paid to each instance in a bag. The method is applied to the detection of intracranial hemorrhage (ICH) on head CT scans. The results show that this novel SA-DMIL: (a) achieves better performance than the non-smooth attention MIL at both scan (bag) and slice (instance) levels;(b) learns spatial dependencies between slices;and (c) outperforms current state-of-the-art MIL methods on the same ICH test set. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

引用

International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

来源：评论

学校读者我要写书评

暂无评论

GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

引用

arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

来源：评论

学校读者我要写书评

暂无评论

Gland Segmentation Via Dual Encoders and Boundary-Enhanced Attention

Gland Segmentation Via Dual Encoders and Boundary-Enhanced A...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huadeng Wang Jiejiang Yu Bingbing Li Xipeng Pan Zhenbing Liu Rushi Lan Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Pathology Ganzhou Municipal Hospital Ganzhou China

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：