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检索条件"任意字段=5th International Conference on Pattern Recognition in Bioinformatics"

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5th IAPR international conference on pattern recognition in bioinformatics, PRIB 2010

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5th IAPR international conference on pattern recognition in bioinformatics, PRIB 2010

ISBN: (纸本)9783642160004

the proceedings contain 38 papers. the special focus in this conference is on pattern recognition in bioinformatics. the topics include: An algorithm to find all identical motifs in multiple biological sequences;discovery of non-induced patterns from sequences;exploring homology using the concept of three-state entropy vector;A maximum-likelihood formulation and EM algorithm for the protein multiple alignment problem;polynomial supertree methods revisited;enhancing graph database indexing by suffix tree structure;Semi-supervised graph embedding scheme with active learning (SSGEAL): Classifying high dimensional biomedical data;iterated local search for biclustering of microarray data;Machine learning study of DNA binding by transcription factors from the LacI family;Biologically-aware latent dirichlet allocation (BaLDA) for the classification of expression microarray;measuring the quality of shifting and scaling patterns in biclusters;frequent episode mining to support pattern analysis in developmental biology;Time series gene expression data classification via L1-norm temporal SVM;Sub-grid and spot detection in DNA microarray images using optimal multi-level thresholding;quantification of cytoskeletal protein localization from high-content images;pattern recognition for high throughput zebrafish imaging using genetic algorithm optimization;consensus of ambiguity: theory and application of active learning for biomedical image analysis;semi-supervised learning of sparse linear models in mass spectral imaging;A matrix algorithm for RNA secondary structure prediction;Joint loop end modeling improves covariance model based non-coding RNA gene search;exploiting long-range dependencies in protein β-sheet secondary structure prediction;alpha helix prediction based on evolutionary computation;an On/Off lattice approach to protein structure prediction from contact maps;preface.

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pattern recognition in bioinformatics - 5th IAPR international conference, PRIB 2010, Proceedings

Pattern Recognition in Bioinformatics - 5th IAPR Internation...

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5th IAPR international conference on pattern recognition in bioinformatics, PRIB 2010

ISBN: (纸本)364216000X

the proceedings contain 38 papers. the topics discussed include: sequence-based prediction of protein secretion success in aspergillus niger;machine learning study of DNA binding by transcription factors from the lacI family;joint loop end modeling improves covariance model based non-coding RNA gene search;structured output prediction of anti-cancer drug activity;optimization algorithms for identification and genotyping of copy number polymorphisms in human populations;preservation of statistically significant patterns in multiresolution 0-1 data;the complexity and application of syntactic pattern recognition using finite inductive strings;an algorithm to find all identical motifs in multiple biological sequences;discovery of non-induced patterns from sequences;exploring homology using the concept of three-state entropy vector;and maximum-likelihood formulation and EM algorithm for the protein multiple alignment problem.

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EB-SNN: An Ensemble Binary Spiking Neural Network for Visual recognition 27th

EB-SNN: An Ensemble Binary Spiking Neural Network for Visua...

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27th international conference on pattern recognition, ICPR 2024

作者： Li, Xinjie Tang, Jianxiong Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangzhou510555 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031781858

In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits their use on resource-limited devices. In this paper, we propose an Ensemble Binary Spiking Neural Network (EB-SNN) for accurate and memory-friendly visual recognition. the EB-SNN is modeled by Ensemble Binary Weights (EBW) module, which integrates multiple binary weights for lightweight SNN modeling. Meanwhile, we propose Knowledge Alignment Strategy to ensure that the EB-SNN can approximate a well-trained SNN for good performance. Experimental results show that the EB-SNN can achieve accuracy of 95.39% on CIFAR10, using 9.3% memory of full-precision SNN. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Local binary pattern

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ICPR 2024 Competition on Word Image recognition from Indic Scene Images 27th

ICPR 2024 Competition on Word Image Recognition from Indic S...

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27th international conference on pattern recognition, ICPR 2024

作者： Lunia, Harsh Mondal, Ajoy Jawahar, C.V. Centre for Vision Information Technology International Institute of Information Technology Hyderabad500032 India

ISBN: (纸本)9783031801389

Scene text recognition has historically concentrated on English, with limited advancements in developing solutions that perform well across multiple languages. Previous efforts in multilingual scene text recognition have predominantly targeted languages with considerable syntactic and semantic differences. However, Indian languages, while diverse, share numerous common features that remain largely underutilized. this competition aims to address the often-overlooked challenge of scene text recognition within the Indian context and to advance robust word image recognition across ten Indian languages. the dataset provided for this competition is one of the most comprehensive multilingual datasets, encompassing 10 languages, each with 17,500 training samples, 2,500 validation samples and 5,000 test word-image samples. the task was to correctly recognize the word-images, for which we received forty-nine registrations and five final submissions from industrial and research communities. the winning team achieved an average Character recognition Rate (CRR) of 92.85% and a Word recognition Rate (WRR) of 84.01% across the ten languages. this paper details the proposed dataset and summarizes the submissions for the competition- WIRIndic-2024. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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A New Impressive and Expressive Features Based Model for Personality Traits Identification 27th

A New Impressive and Expressive Features Based Model for Per...

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27th international conference on pattern recognition, ICPR 2024

作者： Biswas, Kunal Palaiahnakote, Shivakumara Pal, Umapada Chanda, Sukalpa Wu, Xiao-Jun Jadavpur University Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Østfold University College Halden Norway Jiangnan University Wuxi China

ISBN: (纸本)9783031781858

Personality traits identification is challenging due to unpredictable changes in the foreground and background of images. In this work, we propose a new deep learning model for personality traits image classification by exploiting visual and textual information from dating images. We believe that there is a strong correlation between the features (visual + textual) of dating images and personality traits images. the features which draw attention are defined as impressive features of dating images and features that convey emotions are expressive features of personality traits images. this observation motivated us to combine the features of dating images to improve the performance of personality traits images classification. To affirm the above observation, we propose multiple convolutional layers followed by max pool layers which extract features from dating and personality traits images simultaneously. To integrate the strengths of impressive and expressive features, the proposed work introduces a dual fusion approach, which fuses features and modalities at different levels. the experiments are conducted on different standard datasets of personality traits images to demonstrate the effectiveness of impressive features in terms of personality trait image classification. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Augmented Reality-Assisted Environment for Medical Education: An Experience of Interactive and Immersive Learning 27th

Augmented Reality-Assisted Environment for Medical Educatio...

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27th international conference on pattern recognition, ICPR 2024

作者： Puthannadathil Reghunatha Kumar, Vikas Kujur, Anurag Ganguly, Bishnu Behera, Santosh Kumar Dash, Ajaya Kumar National Institute of Technology Calicut Kerala Kozhikode India International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

ISBN: (纸本)9783031801358

Traditional methods of medical education often face challenges such as limited visual representation, lack of interactive experiences, and outdated technological integration. Generally, medical education often relies on static images or diagrams to represent complex anatomical structures, which can hinder students’ understanding and retention of information. Additionally, the lack of interactive experiences restricts students’ interaction and manipulation of anatomical models, creating a less engaging learning environment. this research offers a real-time, seamless method that tackles the noted difficulties by utilising AR technology. To give students a dynamic and engaging learning experience, the suggested AR-based solution seeks to project 3D human organs onto a tracked human body. Apart from that the user can interact with the superimposed organs in real-time using natural gestures and image markers. the proposed AR system leverages advanced technologies including a vision transformer for precise image recognition, deep learning techniques for human hand gesture recognition, and human pose tracking. By integrating these components, the system enables the accurate projection of organs onto a real-world environment, synchronized with the user’s movements. Interactions with the virtual organs are facilitated through intuitive gestures, allowing users to manipulate, scale, and rotate the projected organs effortlessly. To sum up, incorporating augmented reality into medical education presents a viable way to get around current obstacles and give students a more dynamic, immersive, and interesting educational experience. By promoting a greater comprehension and recall of intricate anatomical concepts, this method has the potential to transform medical education. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Research on the Application of Deep Learning-Based Image recognition Algorithm in Signal Processing

Research on the Application of Deep Learning-Based Image Rec...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Jing Lv Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learning the signal image. then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data.

关键词： Deep learning Training Image recognition Signal processing algorithms Generative adversarial networks Generators Robustness pattern recognition Image denoising Signal to noise ratio

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UNeXt++: A Serial-Parallel Hybrid UNeXt for Rapid Medical Image Segmentation 27th

UNeXt++: A Serial-Parallel Hybrid UNeXt for Rapid Medical I...

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27th international conference on pattern recognition, ICPR 2024

作者： Li, Yan Wang, Juelin Deng, Yunteng Li, Binyang Hu, Junlin School of Cyber Science and Engineering University of International Relations Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunication Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9783031781032

Recently, a growing interest has been seen in rapid medical image segmentation for point-of-care applications. UNeXt, a convolutional multilayer perceptron (MLP)-based rapid medical image segmentation network has shown an outstanding performance in single-organ segmentation. However, there is still a large room for improvement in multi-organ segmentation by exploring sufficient information from a global view. To this end, we propose UNeXt++, a more powerful framework that adds a lightweight Serial-Parallel Hybrid Attention module named SPHTension to the UNeXt. the proposed SPHTension is designed to assist in the detection and localization of lesion tissue by extracting image local features and global semantic context through parallel structures, respectively. these structures play distinct roles in instance segmentation. Furthermore, we introduce the Attentional Feature Fusion (AFF) approach, which simultaneously cascades learning blocks to fuse and optimize the feature representation. the proposed hybrid architecture is capable of simultaneously focusing on local and global features in different regions, effectively integrating them to sense the location and edges of lesion tissues, and performing accurate segmentation. It is noteworthy that UNeXt++ is capable of efficiently aggregating global representations by adding only a very small number of parameters. Experimental results demonstrate that our UNeXt++ outperforms UNeXt in terms of segmentation performance on the multi-organ segmentation dataset Synapse and three single-organ segmentation datasets. this improvement is observed to be between 5% and 18%, while the computational cost is reduced by 17% and the amount of parameters is reduced by 19%. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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PROCEEDINGS - international conference ON pattern recognition, 5th, 1980.

PROCEEDINGS - INTERNATIONAL CONFERENCE ON PATTERN RECOGNITIO...

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Proc - Int Conf on pattern recognition, 5th

作者： Anon

this conference proceedings contains 294 papers. Two hundred forty-five papers are indexed separately. Topics covered include: remote sensing, speech recognition;line drawing and contour analysis;nearest neighbor techniques;image processing;hardware concepts and systems;biomedical applications;clustering and learning;robotics;decision and discrimination theories;text processing;texture analysis;pattern recognition;coding and filtering;feature analysis;geometric and statistical methods;and, shape description and analysis.

关键词： pattern recognition systems

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th international conference on pattern recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. this research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. the usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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