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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1867 条记录，以下是41-50 订阅

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Wide Line Segments Detection in Grey-Level Images via Guided Scale Space Radon Transform

Wide Line Segments Detection in Grey-Level Images via Guided...

引用

international Image Processing, Applications and Systems conference (IPAS)

作者： Aicha Baya Goumeidane Djemel Ziou Nafaa Nacereddine Nawal Yala Research Center in Industrial Technologies CRTI Algiers Algeria Département D’Informatique Université de Sherbrooke Québec Canada

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Line segment detection is a fundamental procedure in computer vision, pattern recognition, and image analysis applications. the paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. the method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. the method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising.

关键词： Radiography Image segmentation Image analysis Error analysis Welding Noise Transforms Robustness Iron pattern recognition

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iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection 27th

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detec...

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27th international conference on pattern recognition, ICPR 2024

作者： Jiang, Jian-Jian Wu, Xiao-Ming Chen, Zibo Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031781124

For 6-DoF grasp detection, we aim at introducing a new interactive 2D-3D framework which filters out irrelevant information and makes both modalities collaborate effectively to generate robust grasps and accelerate inference speed greatly. this cannot be accomplished by existing works on learning to grasp that merely utilize 3D point clouds or leverage both 2D textures and 3D point clouds. Our framework is called iGrasp, a novel three-step design between 2D textures and 3D point clouds, where the interaction modelling enhances both modalities. Concretely, we propose the 2D-to-3D interaction to leverage objectness masks generated from 2D textures to filter out target-irrelevant information in 3D point clouds. then, we introduce the 3D-to-2D interaction to leverage structural priors from 3D point cloud features with cross-attention and cylinder grouping to refine 2D texture features. Finally, we combine the refined 2D texture features and 3D point cloud features for generating high-quality 6-DoF grasp poses. Our experiments on the large-scale real-world dataset, namely GraspNet-1Billion, demonstrate that iGrasp surpasses state-of-the-art methods by 4.66/3.53 mAP on RealSense/Kinect and reduces the inference time by 28%. Real-world experiments further verify the effectiveness of iGrasp. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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ICPR 2024 Competition on Beyond Visible Spectrum: AI for Agriculture 27th

ICPR 2024 Competition on Beyond Visible Spectrum: AI for A...

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27th international conference on pattern recognition, ICPR 2024

作者： Han, Liangxiu Huang, Wenjiang Zhang, Xin Dong, Yingying Sobeih, Tam Lin, Yufan Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom Key laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Institute of Data Science National Cheng Kung University Taiwan70101 China

ISBN: (纸本)9783031801389

the ICPR 2024 Competition on "Beyond Visible Spectrum: AI for Agriculture" presents an exciting opportunity for researchers and practitioners to advance computer vision techniques in agricultural crop disease monitoring. By leveraging the vast multi/hyper spectral remote sensing datasets, participants are encouraged to develop innovative machines/deep learning algorithms. the primary objective is to improve the accuracy and efficiency of crop disease identification, thereby fostering sustainable agricultural practices and facilitating global knowledge generation for enhanced food security. this challenge presents participants with the opportunity to make significant contributions to the advancement of precision farming and crop monitoring techniques. By improving the machines/deep learning models, the competition aims to drive forward the capabilities of agricultural technology, making it more efficient, accurate, and sustainable. A total of 190 people from more than 10 countries in Europe, Asia and North America entered the competition by the deadline date, with a total of 126 submissions of results. the rank1 team obtained an accuracy of 73.3% in crop disease classification task with hyperspectral dataset. In the self-supervised learning task, the rank1 team obtained 79.2% accuracy. We report the leader-boards of the competition in this report and present three innovative approaches of the participants. the ICPR 2024 Competition on "Beyond Visible Spectrum" is not just a competition;it is a call to action for the computer research community to bring about transformative changes in how agricultural data is analyze and utilized, paving the way for smarter, more sustainable farming practices that can benefit communities worldwide. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Hybrid Human Interaction recognition Using Feature Extraction and Deep Neural Network

Hybrid Human Interaction Recognition Using Feature Extractio...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Human recognition have become interested in human interactions in images. Interaction recognition is a hot topic in many research fields. In this study, we propose a system for recognizing activities using a Deep Neural Network (DNN). First, we improve the clarity of video frames by transforming them to HSI color. We also use Mean filters to remove any noise. We use Multiple Object Tracking (MOT) and Statistical Region Merging techniques to extract silhouettes. In the feature extraction process, we use Local Binary patterns (LBP), SIFT, and Local Phase Quantization (LPQ). We apply the Particle Swarm Optimization (PSO) algorithm to identify the most important features that best represent the data structure. We then input these selected features into the DNN for classification into different human interactions. We tested this approach on the SBU Interaction dataset, and achieved a recognition rate of $\mathbf{9 2 \%}$.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Biological system modeling Surveillance Artificial neural networks Feature extraction pattern recognition Classification algorithms

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6th international conference on Smart Computing and Informatics, SCI 2024

6th International Conference on Smart Computing and Informat...

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6th international conference on Smart Computing and Informatics, SCI 2024

ISBN: (纸本)9789819619801

the proceedings contain 46 papers. the special focus in this conference is on Smart Computing and Informatics. the topics include: Resilient Domain Authentication Framework for Enhancing Digital Identity Security;traffic Sign Detection with pattern recognition Techniques Using Image Processing;exploring Advanced Techniques in Natural Language Processing and Machine Learning for In-depth Analysis of Insurance Claims;Network Intrusion Detection with SMOTE-ENN and Deep Learning Techniques;leveraging Transfer Learning to Enhance Location Accuracy in Mapping Services: A Case Study of Google Maps;assessment of Enhanced Email Spam Detection System through Machine Learning Algorithms;machine Learning Methods for Predicting Traffic Congestion Forecasting;hybridization of Computational Intelligence Algorithm for Scheduling of Tasks and Balancing of Load in Cloud Network;MDSV: Mobs Detection by Enhanced Fused Feature Base Deep Neural Network from Surveillance Camera;ioT-Based Solution for Enhanced Tracking of Individuals Living with Dementia;a Novel Task Scheduling Algorithm in Heterogeneous Multi-cloud Environment;Evaluating the Integration and Usage of AI in Higher Education;evaluating the Connectional Benefits of Artificial Intelligence in the Digital Classroom;Influence of AI as an Aspect of Modern Education Era in Present World;Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection;the Advancement and Utilization of Artificial Intelligence and Machine Learning in the Financial Industry and Its Impact on Macro and Microeconomics;analysis on the Cutting-Edge Approach to Assess Artificial Intelligence’s Educational Consequences in Contemporary Studies;data Analytics in Sales and Marketing: A Comprehensive Methodology for Business Analysts;Wireless Energy Transfer for UAV (Drone) Using Machine Learning.

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Physical Exergames Movements and pattern recognition using Convolutional Neural Network

Physical Exergames Movements and Pattern Recognition using C...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. the developed approach uses image processing and machine learning tools to correctly extract and classify the features. the framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. Image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. thirdly, the Convolutional Neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. the proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： Image segmentation Machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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pattern recognition in bioinformatics 2012

引用

丛书名： Lecture Notes in Computer Science

2012年

作者： Tetsuo Shibuya Hisashi Kashima Jun Sese Shandar Ahmad

ISBN: (数字)9783642341236

ISBN: (纸本)9783642341229

this book constitutes the refereed proceedings of the 7th international conference on pattern recognition in bioinformatics, PRIB 2012, held in Tokyo, Japan, in November 2012. the 24 revised full papers presented were carefully reviewed and selected from 33 submissions. their topics are widely ranging from fundamental techniques, sequence analysis to biological network analysis. the papers are organized in topical sections on generic methods, visualization, image analysis, and platforms, applications of pattern recognition techniques, protein structure and docking, complex data analysis, and sequence analysis.

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Artificial Neural Networks in pattern recognition: 6th iapr TC 3 internationalWorkshop, ANNPR 2014 Montreal, QC, Canada, October 6-8, 2014 Proceedings 6

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

作者： El Gayar, Neamat Schwenker, Friedhelm Suen, Ching Cairo University Orman Giza Egypt Ulm University Institute for Neural Information Processing Ulm Germany Concordia University Department of Computer Science and Software Engineering MontralQC Canada

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pattern recognition: 6th Mexican conference, MCPR 2014 Cancun, Mexico, June 25-28, 2014 Proceedings 6th

Pattern Recognition: 6th Mexican Conference, MCPR 2014 Cancu...

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6th Mexican conference on pattern recognition, MCPR 2014

作者： Martínez-Trinidad, José Francisco Carrasco-Ochoa, Jesús Ariel Olvera-López, José Arturo Salas-Rodríguez, Joaquín Suen, Ching Y. Sta. Maria Tonantzintla Mexico Puebla Mexico Querétaro Mexico Concordia University MontrealQC Canada

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pattern recognition in High-Content Cytomics Screens for Target Discovery - Case Studies in Endocytosis

Pattern Recognition in High-Content Cytomics Screens for Tar...

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6th iapr international conference on pattern recognition in bioinformatics

作者： Cao, Lu Yan, Kuan Winkel, Leah de Graauw, Mado Verbeek, Fons J. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Leiden Univ Toxicol Leiden Amsterdam Ctr Drug Res Leiden Netherlands

ISBN: (纸本)9783642248542

Finding patterns in time series of images requires dedicated approaches for the analysis, in the setup of the experiment, the image analysis as well as in the pattern recognition. the large volume of images that are used in the analysis necessitates an automated setup. In this paper, we illustrate the design and implementation of such a system for automated analysis from which phenotype measurements can be extracted for each object in the analysis. Using these measurements, objects are characterized into phenotypic groups through classification while each phenotypic group is analyzed individually. the strategy that is developed for the analysis of time series is illustrated by a case study on EGFR endocytosis. Endocytosis is regarded as a mechanism of attenuating epidermal growth factor receptor (EGFR) signaling and of receptor degradation. Increasingly, evidence becomes available showing that cancer progression is associated with a defect in EGFR endocytosis. Functional genomics technologies combine high-throughput RNA interference with automated fluorescence microscopy imaging and multi-parametric image analysis, thereby enabling detailed insight into complex biological processes, like EGFR endocytosis. the experiments produce over half a million images and analysis is performed by automated procedures. the experimental results show that our analysis setup for high-throughput screens provides scalability and robustness in the temporal analysis of an EGFR endocytosis model.

关键词： phenotype measurement image analysis classification EGFR endocytosis

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