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 cla...
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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.
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- substantiatio...
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.
The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learningmachine (ELM) by adding a positive value to the diagonal of HT H or HHT, where H is the...
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ISBN:
(纸本)9789898425980
The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learningmachine (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.
Nowadays, the cultural heritage understanding has become invaluable for a better analysis of history and traditions worldwide. In the last years, machinelearning, intelligent systems and statistical analysis played a...
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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...
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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 machinelearning (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 patternrecognition 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 patternrecognition 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.
作者:
Byun, HLee, SWYonsei 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 patternrecognition applications of Support Vector machines (SVMs). Since SVMs show good generalization performance on many real-life data and the approach is properly motivated t...
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In this paper, we present a survey on patternrecognition 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 patternrecognition applications.
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...
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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 machinelearning. A proposed work is design to increase the strategy for utilizing the techniques of machinelearning. 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 machinelearning. 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 machinelearning. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1stinternational Con-ference on Computations in Materials and Applied Engineering - 2021.
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 g...
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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.
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...
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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.
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 star...
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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.
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