检索结果-内蒙古大学图书馆

IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 355-367页

作者： Cheng Cai Zhaohong Deng Andong Li Yun Zuo Haoran Chen Zhisheng Wei Lei Wang Xiaoyong Pan Hong-Bin Shen Dong-Jun Yu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai China School of Biotechnology Jiangnan University Wuxi China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

The development of intelligent methods capable of predicting protein-ligand binding sites has become a popular research field. Recently, deep learning based methods have been proposed as a promising solution for this task. However, some limitations still exist. For example, the network structure is not optimized for predicting protein binding pockets, which limits the model's capabilities. To address the aforementioned challenges, a novel method called CATransUnetLPB is proposed, in which a new network structure named CATransUnet is designed. The proposed CATransUnet combines CNN and Transformer models to accurately segment binding pocket regions from protein 3D structures. It outperforms existing representative methods on three test sets, demonstrating the effectiveness of optimizing the deep network model for detecting protein ligand binding pockets. Furthermore, we conduct thorough analysis on applying data augmentation to protein data structure and confirm that such technique can enhance the model's generalization ability, thereby ensuring good performance on new protein structures. Moreover, experiments show that the predicted binding pockets from our model can complement the results obtained from other methods. This suggests that integrating our method with existing approaches could further improve the prediction of protein-ligand binding pockets.

关键词： Proteins Three-dimensional displays Probes Vectors image segmentation Deep learning Atoms Surface treatment Prediction algorithms Training

来源：评论

学校读者我要写书评

暂无评论

Consistency-Guided Adaptive Alternating Training for Semi-Supervised Salient Object Detection

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Liyuan Liu, Wei Wang, Hua Jeon, Sang-Woon Jiang, Yunliang Zheng, Zhonglong Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Victoria University Institute for Sustainable Industries and Liveable Cities College of Engineering and Science MelbourneVIC8001 Australia Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

This paper presents a novel approach that leverages two models to integrate features from numerous unlabeled images, addressing the challenge of semi-supervised salient object detection (SSOD). Unlike conventional methods that rely on selecting high-quality pseudo labels, our method identifies the model that produces consistent predictions for original images and their color transformation versions from two models to infer reliable pseudo labels for all unlabeled images, improving the diversity of the training set. Specifically, we propose adaptive selection indicators to quantify prediction differences and guide the updates of the two models using the unlabeled set alternatively. Initially, two models used in our framework are trained on the labeled set. Once the adaptive selection indicator conditions are satisfied, one model is designated as the proxy, generating pseudo labels, while the other serves as the saliency model, which is further trained using these pseudo labels. Subsequently, the updated saliency model optimizes the proxy model's parameters according to another adaptive selection indicator. Experimental results and ablation studies on six benchmark salient object detection datasets confirm the effectiveness and robustness of our method. Our approach achieves performance comparable to recent fully supervised methods while using only one eighth of the labeled data, demonstrating its potential for efficient and scalable SSOD. © 2025 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal patterns and Semantics

arXiv

引用

arXiv 2025年

作者： Tang, Jialiang Chen, Shuo Gong, Chen Zhang, Jing Tao, Dacheng School of Computer Science and Engineering Nanjing University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore School of Intellgence Science and Technology Nanjing University China Department of Automation Institute of Image ProceProceedingssing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science Wuhan University China

Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs), with their powerful in-contextual modeling capabilities, hold significant potential for TSF. However, existing LLM-based methods usually perform suboptimally because they neglect the inherent characteristics of time series data. Unlike the textual data used in LLM pre-training, the time series data is semantically sparse and comprises distinctive temporal patterns. To address this problem, we propose LLM-PS to empower the LLM for TSF by learning the fundamental patterns and meaningful Semantics from time series data. Our LLM-PS incorporates a new multi-scale convolutional neural network adept at capturing both short-term fluctuations and long-term trends within the time series. Meanwhile, we introduce a time-to-text module for extracting valuable semantics across continuous time intervals rather than isolated time points. By integrating these patterns and semantics, LLM-PS effectively models temporal dependencies, enabling a deep comprehension of time series and delivering accurate forecasts. Intensive experimental results demonstrate that LLM-PS achieves state-of-the-art performance in both short- and long-term forecasting tasks, as well as in few- and zero-shot settings. © 2025, CC BY.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Efficient image Super-Resolution With Feature Interaction Weighted Hybrid Network

引用

IEEE Transactions on Multimedia 2025年 27卷 2256-2267页

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing100080 China Shanghai University School of Communication and Information Engineering Shanghai200444 China Nanjing University of Posts and Telecommunications IVIPLab Institute of Advanced Technology Nanjing210046 China Ministry of Education Key Laboratory of Artificial Intelligence Shanghai200240 China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou215006 China Nanjing University of Science and Technology School of Computer Science and Technology Nanjing210094 China Westlake University Research Center for Industries of the Future School of Engineering Hangzhou310024 China OPPO Research SeattleWA98101 United States National Tsing Hua University Department of Electrical Engineering Institute of Communications Engneering Hsinchu300044 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. © 1999-2012 IEEE.

关键词： Transformers Convolutional neural networks Computational modeling Feature extraction Training Superresolution Adaptation models computer architecture Telecommunications image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Deep Learning in Palmprint recognition-A Comprehensive Survey

arXiv

引用

arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

CONTROLLING A computer VIA FACIAL ASPECT

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1995年第4期25卷 669-677页

作者： BALLARD, P STOCKMAN, GC Pattern Recognition & Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

Control of a computer workstation via face position and facial gesturing would be an important advance for people with hand or body disabilities as well as for all users. Steps toward realization of such a system are reported here. A computer system has been developed to track the eyes and the nose of a subject and to compute the direction of the face. Face direction and movement is then used to control the cursor. Test results show that the resulting system is usable, although several improvements are needed.

关键词： Hand Workstations Nose Eye computer systems Persons

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Color-texture image Segmentation

引用

Journal of Shanghai Jiaotong university(science) 2008年第1期13卷 71-75页

作者：郁生阳张艳王永刚杨杰 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Department of Computer Science Nanjing University of Science and Technology

The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.

关键词： color-texture segmentation J value segmentation （JSEG） photometric color invariance edge detection region growing

来源：评论

学校读者我要写书评

暂无评论

Improving depth resolution of diffuse optical tomography with an exponential adjustment method based on maximum singular value of layered sensitivity

引用

Chinese Optics Letters 2008年第12期6卷 886-888页

作者：牛海晶郭平宋晓东蒋田仔 Image Processing and Pattern Recognition Laboratory Beijing Normal University School of Computer Science and Technology Beijing Institute of Technology School of Aerospace Science and Engineering Beijing Institute of Technology National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

The sensitivity of diffuse optical tomography （DOT） imaging exponentially decreases with the increase of photon penetration depth, which leads to a poor depth resolution for DOT. In this letter, an exponential adjustment method （EAM） based on maximum singular value of layered sensitivity is proposed. Optimal depth resolution can be achieved by compensating the reduced sensitivity in the deep medium. Simulations are performed using a semi-infinite model and the simulation results show that the EAM method can substantially improve the depth resolution of deeply embedded objects in the medium. Consequently, the image quality and the reconstruction accuracy for these objects have been largely improved.

关键词： Diagnostic radiography Electroabsorption modulators image quality Light absorption Medical imaging Optical tomography

来源：评论

学校读者我要写书评

暂无评论

A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

引用

Chinese Physics B 2016年第10期25卷 76-88页

作者：柴秀丽甘志华路杨张苗辉陈怡然 School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Department of Electrical and Computer Engineering University of Pittsburgh School of Software Henan University Research Department Henan University

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

关键词： genetic recombination memristive hyperchaotic system color image encryption confusion diffusion

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：