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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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Recurrent and feedforward backpropagation for time independent pattern recognition

Recurrent and feedforward backpropagation for time independe...

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international Joint conference on Neural Networks (IJCNN)

作者： H. Behrens D. Gawronska J. Hollatz B. Schurmann Institut für Informatik TU München Munchen Germany Universität der Bundeswehr Munich Munchen Germany Corporate Research and Development Siemens AG Munchen Germany

Based on a unified description of neural algorithms for time-independent pattern recognition, the authors discuss the generalization ability of a three-layer perceptron for recurrent backpropagation depending on the number of learning epochs and the steepness of the neurons' threshold function. Moreover, the suitability of recurrent and feedforward backpropagation learning algorithms for implementation on multiprocessor systems is investigated. In contrast to the common belief that recurrent back-propagation is more computationally intensive than feedforward backpropagation, the present results for optical character recognition indicate that this need not be the case if the steepness of the threshold function is appropriately chosen. This makes recurrent backpropagation a suitable candidate for time-independent pattern recognition.< >

关键词： pattern recognition Backpropagation algorithms Neurons Multiprocessing systems Equations Neurofeedback Neural networks Computer architecture Concurrent computing Image processing

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Extraction of Discriminative patterns from Skeleton Sequences for Human Action recognition

Extraction of Discriminative Patterns from Skeleton Sequence...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Tran Thang Thanh Fan Chen Kazunori Kotani Hoai-Bac Le School of Information Science Japan Advanced Institute of Science and Technology Japan Faculty of Information Technology University of Science Ho Chi Minh Vietnam

Emergence of novel techniques, such as the invention of MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach in this paper to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a series of unit actions, each of which is represented by a pattern. Given a skeleton sequence, we first automatically extract the key-frames for unit actions, and then label them as different patterns. We further use a statistical metric to evaluate the discriminative capability of each pattern, and define the bag of reliable patterns as local features for action recognition. Experimental results show that the extracted local descriptors could provide very high accuracy in the action recognition, which demonstrate the efficiency of our method in extracting discriminative patterns.

关键词： Feature extraction Three dimensional displays Skeleton Histograms Shape Videos pattern recognition

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A new integration scheme with multilayer perceptron networks for handwritten Chinese character recognition

A new integration scheme with multilayer perceptron networks...

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international conference on pattern recognition

作者： C.H. Wang B.H. Xiao R.W. Dai Institute of Automation Chinese Academy and Sciences China

ISBN: (纸本)0769507506

In this paper, a new integration scheme with multilayer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition problem. The idea of meta-synthesis is emphasized in this scheme, human intelligence and computer capabilities are combined together through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme has much better performance and provides a promising way of applying MLP to large vocabulary classification.

关键词： Multilayer perceptrons pattern recognition Character recognition Humans Supervised learning Man machine systems Vocabulary Feature extraction Automation High performance computing

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Monogenic Binary pattern (MBP): A Novel Feature Extraction and Representation Model for Face recognition

Monogenic Binary Pattern (MBP): A Novel Feature Extraction a...

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international conference on pattern recognition

作者： Meng Yang Lei Zhang Lin Zhang David Zhang Biometric Research Center Department of Computing Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9781424475421

A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robust face representation and recognition. MBP consists of two parts: one is monogenic magnitude encoded via uniform LBP, and the other is monogenic orientation encoded as quadrant-bit codes. The HMBP is established by concatenating the histograms of MBP of all sub-regions. Compared with the well-known and powerful Gabor filtering based LBP schemes, one clear advantage of HMBP is its lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. The experimental results on the AR and FERET face databases validate that the proposed MBP algorithm has better performance than or comparable performance with state-of-the-art local feature based methods but with significantly lower time and space complexity.

关键词： Face Histograms Face recognition Feature extraction Databases Complexity theory Filtering

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5W1H Extraction With Large Language Models

5W1H Extraction With Large Language Models

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international Joint conference on Neural Networks (IJCNN)

作者： Cao, Yang Lan, Yangsong Zhai, Feiyan Li, Piji Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

The extraction of essential news elements through the 5W1H framework (What, When, Where, Why, Who, and How) is critical for event extraction and text summarization. The advent of Large language models (LLMs) such as ChatGPT presents an opportunity to address language-related tasks through simple prompts without fine-tuning models with much time. While ChatGPT has encountered challenges in processing longer news texts and analyzing specific attributes in context, especially answering questions about What, Why, and How. The effectiveness of extraction tasks is notably dependent on highquality human-annotated datasets. However, the absence of such datasets for the 5W1H extraction increases the difficulty of finetuning strategies based on open-source LLMs. To address these limitations, first, we annotate a high-quality 5W1H dataset based on four typical news corpora (CNN/DailyMail, XSum, NYT, RAMDS);second, we design several strategies from zero-shot/fewshot prompting to efficient fine-tuning to conduct 5W1H aspects extraction from the original news documents. The experimental results demonstrate that the performance of the fine-tuned models on our labelled dataset is superior to the performance of ChatGPT. Furthermore, we also explore the domain adaptation capability by testing the source-domain (e.g. NYT) models on the target domain corpus (e.g. CNN/DailyMail) for the task of 5W1H extraction.

关键词： 5W1H Extraction LLMs Prompting Fine-tuning

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CUDA Implementation of Deformable pattern recognition and its Application to MNIST Handwritten Digit Database

CUDA Implementation of Deformable Pattern Recognition and it...

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international conference on pattern recognition

作者： Yoshiki Mizukami Katsumi Tadamura Jonathan Warrell Peng Li Simon Prince Graduate School of Science and Engineering Yamaguchi University Ube Japan Department of Computing Oxford Brookes University Oxford UK Department of Computer Science University College London London UK

ISBN: (纸本)9781424475421

In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then a regularization technique computes the horizontal and vertical displacements based on these distance maps. The dissimilarity is measured based on the eight-directional derivative of input and prototype images in order to leverage characteristic information on the curvature of line segments that might be lost after the deformation. The prototype-parallel displacement computation on CUDA and the gradual prototype elimination technique are employed for reducing the computational time without sacrificing the accuracy. A simulation shows that the proposed method with the k-nearest neighbor classifier gives the error rate of 0.57% for the MNIST handwritten digit database.

关键词： Prototypes Pixel Error analysis Mathematical model Databases Character recognition

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Short term memory for bipolar temporal patterns

Short term memory for bipolar temporal patterns

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international Joint conference on Neural Networks (IJCNN)

作者： M.D. Tom M.F. Tenorio Sch. of Electr. Eng. Purdue Univ. West Lafayette IN USA

Summary form only given. A study of the short-term memory requirements of temporal pattern recognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hysteresis loop are described by two equations. Generalizing the two equations to two families of curves accommodates loops of various sizes. It is conjectured that this unit is capable of memorizing the entire history of its inputs.< >

关键词： Hysteresis Equations Laboratories pattern recognition Computational modeling Concurrent computing Distributed computing History Neurons

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Research on False Alarm Removal Method for Aircraft Target Detection under Small Sample Conditions 4

Research on False Alarm Removal Method for Aircraft Target D...

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4th IEEE international conference on Image, Vision and computing, ICIVC 2019

作者： Zeng, Qinghao Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Institute of Automation Beijing100190 China

ISBN: (纸本)9781728123257

Aiming at the aircraft target in visible light remote sensing image, this paper proposes a false alarm removal method for target detection under small sample training conditions. First, use data enhancement methods for small sample data to increase the number of samples and improve the generalization ability of features. Then, according to the clear outline of the aircraft target and the obvious characteristics of the edge features, the Haar feature plus AdaBoost algorithm is used to preliminary detect the aircraft target. Finally, the convolutional neural network is used to extract the target features, and the preliminary detection results are classified to remove the false alarm targets and improve the accuracy of the detection. The experimental results show that the detection effect is good in aircraft target detection. © 2019 IEEE.

关键词： Errors

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Multi-objective feature selection with NSGA II

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8th international conference on Adaptive and Natural computing Algorithms, ICANNGA 2007

作者： Hamdani, Tarek M. Won, Jin-Myung Alimi, Adel M. Karray, Fakhri University of Sfax ENIS BP. W-3038 Sfax Tunisia Pattern Analysis and Machine Intelligence Research Group Department of Electrical and Computer Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada

ISBN: (纸本)9783540715894

This paper deals with the multi-objective definition of the feature selection problem for different pattern recognition domains. We use NSGA II the latest multi-objective algorithm developed for resolving problems of multiobjective aspects with more accuracy and a high convergence speed. We define the feature selection as a problem including two competing objectives and we try to find a set of optimal solutions so called Pareto-optimal solutions instead of a single optimal solution. The two competing objectives are the minimization of both the number of used features and the classification error using 1-NN classifier. We apply our method to five databases selected from the UCI repository and we report the results on these databases. We present the convergence of the NSGA II on different problems and discuss the behavior of NSGA II on these different contexts. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Problem solving

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Assessment of soft-tissue sarcomas perfusion using data-driven techniques

Assessment of soft-tissue sarcomas perfusion using data-driv...

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2018 IEEE EMBS international conference on Biomedical and Health Informatics, BHI 2018

作者： Venianaki, M. Karantanas, A. De Bree, E. Maris, T. Kontopodis, E. Nikiforaki, K. Salvetti, O. Marias, K. IMT School for Advanced Studies Lucca Piazza S. Ponziano 6 Lucca55100 Italy Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece Medical School-University of Crete Departments of Medical Imaging and Medical Physics University Hospital Heraklion Greece School of Medicine University of Crete Heraklion Greece Istituto di Scienza e Tecnologie dell'Informazione 'Alessandro Faedo' CNR Area della Ricerca CNR di Pisa Pisa Italy Technological Educational Institute of Crete Department of Informatics Engineering Heraklion Greece

ISBN: (纸本)9781538624050

The present study aims 1. to automatically classify the heterogeneously perfused tumors using dynamic contrast-enhanced (DCE) MRI data from two patients diagnosed with malignant peripheral nerve sheath tumor (MPNST), 2. to compare the differences between the two cases, and 3. to detect the possible presence of hypoxia. A pattern recognition (PR) algorithm has been applied to the data resulting in the identification of areas with different perfusion. The algorithm was proven to be robust since the intensity curves of the PR classified pixels had similar shapes with the curves of the PR identified patterns and were in line with the theoretically expected enhancement patterns of the DCE time-signal curves. © 2018 IEEE.

关键词： Tumors

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