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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是111-120 订阅

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A hierarchical system for recognition, tracking and pose estimation

A hierarchical system for recognition, tracking and pose est...

引用

1st international workshop on machine learning for Multimodal Interaction

作者： Zehnder, P Koller-Meier, E Van Gool, L ETH DITET Comp Vis Lab CH-8092 Zurich Switzerland

ISBN: (纸本)354024509X

This paper presents a new system for recognition, tracking and pose estimation of people in video sequences. It is based on the wavelet transform from the upper body part and uses Support Vector machines (SVM) for classification. recognition is carried out hierarchically by first recognizing people and then individual characters. The characteristic features that best discriminate one person from another are learned automatically. Tracking is solved via a particle filter that utilizes the SVM output and a first order kinematic model to obtain a robust scheme that successfully handles occlusion, different poses and camera zooms. For pose estimation a collection of SVM classifiers is evaluated to detect specific, learned poses.

关键词： pattern recognition

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Proceeding of the 1st international workshop on Image mining Theory and Application, IMTA 2008 - In Conjuction with VIISIGRAPP 2008

Proceeding of the 1st International Workshop on Image Mining...

引用

1st international workshop on Image mining Theory and Applications, IMTA 2008 - In Conjunction with VIISIGRAPP 2008

ISBN: (纸本)9789898111258

The proceedings contain 14 papers. The topics discussed include: descriptive analysis of image data: basic models;media analysis and the algorithm ontology;descriptive approach to medical image analysis- substantiation and interpretation;shape modeling for the analysis of heart deformation patterns;fast multi-view evaluation of data represented by symmetric clusters;search algorithm and the distortion analysis of fine details of real images;a proposal for automatic inference of pressure ulcers grade based on wound images and patient data;an image mining medical warehouse;geo-located image categorization and location recognition;pearling: stroke segmentation with crusted pearl strings;automatic target retrieval in a video surveillance task;and learning probabilistic models for recognizing faces under pose variations.

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Online sequential learning based on enhanced extreme learning machine using leftor right pseudo-inverse

Online sequential learning based on enhanced extreme learnin...

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1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Zong, Weiwei Lan, Yuan Huang, Guang-Bin School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789898425980

The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learning machine (ELM) by adding a positive value to the diagonal of HT H or HHT, where H is the hidden layer output matrix. This paper further extends this enhanced ELM to online sequential learning mode. An online sequential learning algorithm is proposed for SLFNs and other regularization networks, consisting of two formulas for two kinds of scenarios: when initial training data is of small scale or large scale. Performance of proposed online sequential learning algorithm is demonstrated through six benchmarking data sets for both regression and multi-class classification problems.

关键词： Benchmarking

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VIPERC 2019: Visual pattern extraction and recognition for cultural heritage understanding 1

VIPERC 2019: Visual pattern extraction and recognition for c...

引用

1st international workshop on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2019

作者： Amelio, Alessia Ćosović, Marijana Draganov, Ivo Rumenov Janković, Radmila Kabassi, Katerina Tanikić, Dejan University of Calabria DIMES Rende87036 Italy University of East Sarajevo Faculty of Electrical Engineering Lukavica East Sarajevo71126 Bosnia and Herzegovina Technical University of Sofia Faculty of Telecommunications Sofia1756 Bulgaria Mathematical Institute of the Serbian Academy of Sciences and Arts Belgrade11000 Serbia Ionian University Kerkira491 00 Greece University of Belgrade Technical Faculty in Bor Bor19210 Serbia

Nowadays, the cultural heritage understanding has become invaluable for a better analysis of history and traditions worldwide. In the last years, machine learning, intelligent systems and statistical analysis played a key role in the extraction, analysis and identification of patterns from visual data in different real-life aspects. In this context, the main goal of the 1st international workshop on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2019) was presenting the advancement in visual pattern extraction, analysis and recognition aiming to preserve the cultural heritage. The event was organised as an international research forum by the University of Calabria, the University of East Sarajevo, the Technical University of Sofia, the Mathematical Institute of the Serbian Academy of Sciences and Arts, the Ionian University and the University of Belgrade. © 2019 CEUR-WS. All rights reserved.

关键词： Intelligent systems

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Soft Exoskeleton Glove for Hand Assistance Based on Human-machine Interaction and machine learning 1

Soft Exoskeleton Glove for Hand Assistance Based on Human-ma...

引用

1st IEEE international Conference on Human-machine Systems (ICHMS)

作者： Chen, Xiaoshi Gong, Li Zheng, Lirong Zou, Zhuo Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158716

This paper proposes a human machine interaction system in the field of stroke rehabilitation, based on the concept of mirror therapy (MT). It aims to improve the hand motor function of stroke patients, enabling a true synchronization between the affected hand and non-affected hand (healthy hand) for the stroke patient. It consists of a soft exoskeleton glove, a surface electromyography (sEMG) signal collecting armband and machine learning (ML) algorithms. The glove is developed by integrating low-power motors to provide force strength for the hand movement. Unlike the rigid exoskeleton devices, the glove is comfortable to wear and lightweight, so it is more suitable for rehabilitation training of stroke patients in daily life. The armband collects the sEMG signals for pattern recognition by the ML algorithms. In the experiment, four subjects perform 10 hand gestures to collect data for model training. A comparison of data preprocessing is conducted to find the optimal data segmentation method and feature vector sets. A series of pattern recognition algorithms are developed and assessed in different aspects, including prediction accuracy, training time and predicting time. All 10 gestures can be recognized in offline mode with an accuracy up to 99.4%. The control of soft exoskeleton glove in real-time manner is also carried out, and the accuracy is 82.2%. The experiment result demonstrates the feasibility of the proposed system. The innovations and limitations of the work are discussed at the end of the paper.

关键词： soft exoskeleton glove surface electromyography (sEMG) mirror therapy machine learning

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A survey on pattern recognition applications of support vector machines

A survey on pattern recognition applications of support vect...

引用

1st international workshop on pattern recognition with Support Vector machines

作者： Byun, H Lee, SW Yonsei Univ Dept Comp Sci Seodaemun Gu Seoul 120749 South Korea Korea Univ Dept Comp Sci & Engn Seongbuk Ku Seoul 136701 South Korea

In this paper, we present a survey on pattern recognition applications of Support Vector machines (SVMs). Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its various pattern recognition applications.

关键词： Support Vector machines pattern recognition face detection face recognition object recognition handwritten character recognition speech recognition

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Optimization classification of sunflower recognition through machine learning

Optimization classification of sunflower recognition through...

引用

1st international Conference on Computations in Materials and Applied Engineering (CMAE)

作者： Kaur, Rupinder Jain, Anubha Kumar, Sarvesh IIS Univ Dept Comp Sci Jaipur Rajasthan India Integral Univ Lucknow Uttar Pradesh India

In this research paper, our main focus is to design and develop a system for classification and recognition methodology for the acknowledgment and retrieval of a Sunflower flower in the natural environment centralized on the indigenous habitat dependent on a multi-layer method. Further, we design applica-tions for their better classification. To handle a difficult undertaking task, an interdisciplinary cooperation is displayed dependent in the latest advancement methods in software implementation in engineering and innovation implemented by machine learning. A proposed work is design to increase the strategy for utilizing the techniques of machine learning. Final utilization of the Texture Feature, Rst-Invariant Feature, pattern Classification and furthermore utilize the K-Closest Neighbor calculations is done. Firstly, the paper is proposes to study about how to gather a flower images from the natural environment along with their corresponding background and Secondly, the paper focus on the Sunflower classification utility through machine learning. The computerization methods through blossom utilizing through AI system for sunflower utilized the 6-types of sunflower to get the fine yielding of profoundly sprouted sunflower blooms is caught from an advanced camera with a picture. The process of recognition imple-mented carried with 280 pictures. This method used a recognition as well as classification of sunflower by using the k-nearest neighbor image having overall 88.52% accuracy. This designed research paper, we trained the model with information and when concealed information is achieved then the predictive model predicts the Sunflower recognition through trained data supervised technique with machine learning. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st international Con-ference on Computations in Materials and Applied Engineering - 2021.

关键词： machine learning Sunflower Scikit recognition system Rst-Invariant feature pattern classification

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PCA-based data mining probabilistic and fuzzy approaches with applications in pattern recognition

PCA-based data mining probabilistic and fuzzy approaches wit...

引用

1st international Conference on Software and data Technologies

作者： state, Luminita Cocianu, Catalina Vlamos, Panayiotis stefanescu, Viorica Univ Pitesti Dept Comp Sci Caderea Bastliei 45 Bucharest 1 Romania Acad Econ Studies Dept Comp Sci Bucharest 1 Romania Ionian Univ Corfu Greece Acad Econ Studies Dept Math Bucharest 1 Romania

ISBN: (纸本)9728865694

The aim of the paper is to develop a new learning by examples PCA-based algorithm for extracting skeleton information from data to assure both good recognition performances, and generalization capabilities. Here the generalization capabilities are viewed twofold, on one hand to identify the right class for new samples coming from one of the classes taken into account and, on the other hand, to identify the samples coming from a new class. The classes are represented in the measurement/feature space by continuous repartitions, that is the model is given by the family of density functions( f(h))(h is an element of H) where H stands for the finite set of hypothesis (classes). The basis of the learning process is represented by samples of possible different sizes coming from the considered classes. The skeleton of each class is given by the principal components obtained for the corresponding sample.

关键词： principal component analysis fuzzy clustering supervised learning cluster analysis pattern recognition data mining

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Galaxy classification based on deep learning 24

Galaxy classification based on deep learning

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Huang, Ruijie Wu, Haoran Huang, Jiayi Beijing Normal Univ Hong Kong Baptist Univ United AI FST Beijing Peoples R China

ISBN: (纸本)9798400707032

In this study, a deep learning-based galaxy classification method is discussed. The classification of galaxies is important for understanding the formation and evolution of the universe, and traditional classification methods rely on artificial visual analysis, but in the face of large amounts of data, this method is time-consuming and prone to error. In recent years, automated classification methods, especially using deep learning techniques, have gradually come into focus. Deep learning models, particularly convolutional neural networks (CNNS), are capable of automatically extracting and learning complex features in galactic images, enabling efficient and accurate classification. The research plan is to integrate multimodal data, train models with large-scale datasets, and introduce interpretative analysis into the classification process to improve model transparency. Ultimately, the goal is to develop an efficient galactic classification system to support data processing and analysis in the field of astronomy.

关键词： Deep learning Image classification Galaxy Probability distribution

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Concepts learning with fuzzy clustering and relevance feedback

引用

2nd international workshop on machine learning and data mining in pattern recognition

作者： Bhanu, B Dong, A Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

ISBN: (纸本)3540423591

In recent years feedback approaches have been used in relating low-level image features with concepts to overcome the subjective nature of the human image interpretation. Generally, in these systems when the user starts with a new query, the entire prior experience of the system is lost. In this paper, we address the problem of incorporating prior experience of the retrieval system to improve the performance on future queries. We propose a semi-supervised fuzzy clustering method to learn class distribution (meta knowledge) in the sense of high-level concepts from retrieval experience. Using fuzzy rules, we incorporate the meta knowledge into a probabilistic relevance feedback approach to improve the retrieval performance. Results presented on synthetic and real databases show that our approach provides better retrieval precision compared to the case when no retrieval experience is used.

关键词： Fuzzy clustering

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