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Uma metodologia para extração de conhecimento em séries temporais por meio da identificação de motifs e da extração de características; A methodology to extract knowledge from time series using motif identification and feature extraction

Maletzke, André Gustavo
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 30/04/2009 Português
Relevância na Pesquisa
66.2%
Mineração de dados tem sido cada vez mais aplicada em distintas áreas com o objetivo de extrair conhecimento interessante e relevante de grandes conjuntos de dados. Nesse contexto, aprendizado de máquina fornece alguns dos principais métodos utilizados em mineração de dados. Dentre os métodos empregados em aprendizado de máquina destacam-se os simbólicos que possuem como principal contribuição a interpretabilidade. Entretanto, os métodos de aprendizado de máquina tradicionais, como árvores e regras de decisão, não consideram a informação temporal presente nesses dados. Este trabalho propõe uma metodologia para extração de conhecimento de séries temporais por meio da extração de características e da identificação de motifs. Características e motifs são utilizados como atributos para a extração de conhecimento por métodos de aprendizado de máquina. Essa metodologia foi avaliada utilizando conjuntos de dados conhecidos na área. Foi realizada uma análise comparativa entre a metodologia e a aplicação direta de métodos de aprendizado de máquina sobre as séries temporais. Os resultados mostram que existe diferença estatística significativa para a maioria dos conjuntos de dados avaliados. Finalmente...

Seleção supervisionada de características por ranking para processar consultas por similaridade em imagens médicas; Supervised feature selection by ranking to process similarity queries in medical images

Mamani, Gabriel Efrain Humpire
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 05/12/2012 Português
Relevância na Pesquisa
56.23%
Obter uma representação sucinta e representativa de imagens médicas é um desafio que tem sido perseguido por pesquisadores da área de processamento de imagens médicas com o propósito de apoiar o diagnóstico auxiliado por computador (Computer Aided Diagnosis - CAD). Os sistemas CAD utilizam algoritmos de extração de características para representar imagens, assim, diferentes extratores podem ser avaliados. No entanto, as imagens médicas contêm estruturas internas que são importantes para a identificação de tecidos, órgãos, malformações ou doenças. É usual que um grande número de características sejam extraídas das imagens, porém esse fato que poderia ser benéfico, pode na realidade prejudicar o processo de indexação e recuperação das imagens com problemas como a maldição da dimensionalidade. Assim, precisa-se selecionar as características mais relevantes para tornar o processo mais eficiente e eficaz. Esse trabalho desenvolveu o método de seleção supervisionada de características FSCoMS (Feature Selection based on Compactness Measure from Scatterplots) para obter o ranking das características, contemplando assim, o que é necessário para o tipo de imagens médicas sob análise. Dessa forma, produziu-se vetores de características mais enxutos e eficientes para responder consultas por similaridade. Adicionalmente...

Classificação de séries temporais por similaridade e extração de atributos com aplicação na identificação automática de insetos; Classification of time series similarity and feature extraction with application to automatic identification of insects

Silva, Diego Furtado
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/02/2014 Português
Relevância na Pesquisa
56.28%
Um dos grandes desafios em mineração de dados é a integração de dados temporais ao seu processo. Existe um grande número de aplicações emergentes que envolvem dados temporais, incluindo a identificação de transações fraudulentas em cartões de crédito e ligações telefônicas, a detecção de intrusão em sistemas computacionais, a predição de estruturas secundárias de proteínas, a análise de dados provenientes de sensores, entre muitas outras. Neste trabalho, tem-se interesse na classificação de séries temporais que representam sinais de áudio. Como aplicação principal, tem-se interesse em classificar sinais de insetos coletados por um sensor óptico, que deve ser capaz de contar e classificar os insetos de maneira automática. Apesar de serem coletados opticamente, os sinais capturados se assemelham a sinais de áudio. O objetivo desta pesquisa é comparar métodos de classificação por similaridade e por extração de atributos que possam ser utilizados no contexto da classificação de insetos. Para isso, foram empregados os principais métodos de classificação de sinais de áudio, que têm sido propostos para problemas como reconhecimento de instrumentos musicais, fala e espécies animais. Neste trabalho...

Feature extraction and visualization from higher-order CFD data; Extração de estruturas e visualização de soluções de DFC de alta ordem

Pagot, Christian Azambuja
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Tese de Doutorado Formato: application/pdf
Português
Relevância na Pesquisa
66.38%
Métodos de simulação baseados em dinâmica de fluidos computacional (DFC) têm sido empregado em diversas areas de estudo, tais como aeroacústica, dinâmica dos gases, fluidos viscoelásticos, entre outros. Entretanto, a necessidade de maior acurácia e desempenho destes métodos têm dado origem a soluções representadas por conjuntos de dados cada vez mais complexos. Neste contexto, técnicas voltadas à extração de estruturas relevantes (features), e sua posterior visualização, têm um papel muito importante, tornando mais fácil e intuitiva a análise dos dados gerados por simulações. Os métodos de extração de estruturas detectam e isolam elementos significativos no contexto da análise dos dados. No caso da análise de fluidos, estas estruturas podem ser isosuperfícies de pressão, vórtices, linhas de separação, etc. A visualização, por outro lado, confere atributos visuais a estas estruturas, permitindo uma análise mais intuitiva através de sua inspeção visual. Tradicionalmente, métodos de DFC representam suas soluções como funções lineares definidas sobre elementos do domínio. Entretanto, a evolução desses métodos tem dado origem a soluções representadas analiticamente através de funções de alta ordem. Apesar destes métodos apresentarem características desejáveis do ponto de vista de eficiência e acurácia...

Feature extraction in pressure signals for leak detection in water networks

Gamboa-Medina, Maria Mercedes; Reis, Luisa Fernanda Ribeiro; Guido, Rodrigo Capobianco
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Artigo de Revista Científica Formato: 688-697
Português
Relevância na Pesquisa
66.2%
Techniques based on signal analysis for leak detection in water supply systems typically use long pressure and/or flow data series of variable length. This paper presents the feature extraction from pressure signals and their application to the identification of changes related to the onset of a leak. Example signals were acquired from an experimental laboratory circuit, and features were extracted from temporal domain and from transformed signals. Statistical analysis of features values and a classification method were applied. It was verified the feasibility of using feature vectors for distinguish data acquired in the absence or presence of a leak.

Unsupervised feature extraction via kernel subspace techniques

Teixeira, Ana Rita; Tomé, A.M.; Lang, Elmar W.
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
56.2%
This paper provides a new insight into unsupervised feature extraction techniques based on kernel subspace models. The data projected onto kernel subspace models are new data representations which might be better suited for classification. The kernel subspace models are always described exploiting the dual form for the basis vectors which requires that the training data must be available even during the test phase. By exploiting an incomplete Cholesky decomposition of the kernel matrix, a computationally less demanding implementation is proposed. Online benchmark data sets allow the evaluation of these feature extraction methods comparing the performance of two classifiers which both have as input either the raw data or the new representations.; FCT - PhD Scholarship (SFRH/BD/28404/2006); Grants of DAAD

Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

Rosado, Luis; Janeiro, Fernando M.; Ramos, Pedro M.; Piedade, Moisés
Fonte: IEEE Transactions in Instrumentation and Measurement Publicador: IEEE Transactions in Instrumentation and Measurement
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
66.16%
The estimation of the parameters of defects from eddy current nondestructive testing data is an important tool to evaluate the structural integrity of critical metallic parts. In recent years, several works have reported the use of artificial neural networks (ANNs) to deal with the complex relation between the testing data and the defect properties. To extract relevant features used by the ANN, principal component analysis, wavelet decomposition, and the discrete Fourier transform have been proposed. In this paper, a method to estimate dimensional parameters from eddy current testing data is reported. Feature extraction is based on the modeling of the testing data by a template of additive Gaussian functions and nonlinear regressions to estimate their parameters. An ANN was trained using features extracted from a synthetic data set obtained with finite-element modeling of the eddy current probe. The proposed method was applied to both simulated and measured data, providing good estimates.

Lung disease detection using feature extraction and extreme learning machine

Ramalho,Geraldo Luis Bezerra; Rebouças Filho,Pedro Pedrosa; Medeiros,Fátima Nelsizeuma Sombra de; Cortez,Paulo César
Fonte: SBEB - Sociedade Brasileira de Engenharia Biomédica Publicador: SBEB - Sociedade Brasileira de Engenharia Biomédica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2014 Português
Relevância na Pesquisa
66.16%
INTRODUCTION: The World Health Organization estimates that by 2030 the Chronic Obstructive Pulmonary Disease (COPD) will be the third leading cause of death worldwide. Computerized Tomography (CT) images of lungs comprise a number of structures that are relevant for pulmonary disease diagnosis and analysis. METHODS: In this paper, we employ the Adaptive Crisp Active Contour Models (ACACM) for lung structure segmentation. And we propose a novel method for lung disease detection based on feature extraction of ACACM segmented images within the cooccurrence statistics framework. The spatial interdependence matrix (SIM) synthesizes the structural information of lung image structures in terms of three attributes. Finally, we perform a classification experiment on this set of attributes to discriminate two types of lung diseases and health lungs. We evaluate the discrimination ability of the proposed lung image descriptors using an extreme learning machine neural network (ELMNN) comprising 4-10 neurons in the hidden layer and 3 neurons in the output layer to map each pulmonary condition. This network was trained and validated by applying a holdout procedure. RESULTS: The experimental results achieved 96% accuracy demonstrating the effectiveness of the proposed method on identifying normal lungs and diseases as COPD and fibrosis. CONCLUSION: Our results lead to conclude that the method is suitable to integrate clinical decision support systems for pulmonary screening and diagnosis.

Feature Extraction Without Edge Detection

Chaney, Ronald D.
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Formato: 159 p.; 1640697 bytes; 2318330 bytes; application/octet-stream; application/pdf
Português
Relevância na Pesquisa
66.31%
Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.

Lineal Feature Extraction by Parallel Stick Growing

Nelson, Randal C. ; Hunt, Galen C.
Fonte: University of Rochester. Computer Science Department. Publicador: University of Rochester. Computer Science Department.
Tipo: Relatório
Português
Relevância na Pesquisa
66.16%
Finding lineal features in an image is an important step in many object recognition and scene analysis procedures. Previous feature extraction algorithms exhibit poor parallel performance because features often extend across large areas of the data set. This paper describes a parallel method for extracting lineal features based on an earlier sequential algorithm, stick growing. The new method produces results qualitatively similar to the sequential method. Experimental results show a significant parallel processing speed-up attributable to three key features of the method: a large numbers of lock preemptible search jobs, a random priority assignment to source search regions, and an aggressive deadlock detection and resolution algorithm. This paper also describes a portable generalized thread model. The model supports a light-weight job abstraction that greatly simplifies parallel vision programming.

A new method of feature extraction and location derivation in vineyards using point clouds

Gao, D.; Lu, T.F.; Grainger, S.
Fonte: American Society of Agricultural and Biological Engineers Publicador: American Society of Agricultural and Biological Engineers
Tipo: Artigo de Revista Científica
Publicado em //2014 Português
Relevância na Pesquisa
56.19%
An automatic pruning machine is desirable due to the limitations and drawbacks of current labor intensive grapevine pruning methods. Automation mitigates the issue of skilled worker shortages and reduces overall labor cost. To achieve autonomous grapevine pruning accurately and effectively, it is crucial to identify and locate some key features including post, trunk, cordon, and cane in order to open/close cutter and adjust the height of cutter appropriately. In this article, a new method is proposed to automatically identify these features and derive their locations using point clouds. This method combines the advantages of cylinder extraction, density clustering, and skeleton extraction for identification purposes. More importantly, it fills the gap of non-uniformed feature extraction in vineyards using point clouds. The results of applying this method to different data sets obtained from vineyards are presented and its effectiveness is illustrated.; D. Gao, T.-F. Lu, S. Grainger

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

Guerrero-Mosquera, Carlos; Malanda Trigueros, Armando; Iriarte Franco, Jorge; Navia-Vázquez, Ángel
Fonte: Springer; International Federation for Medical and Biological Engineering Publicador: Springer; International Federation for Medical and Biological Engineering
Tipo: info:eu-repo/semantics/acceptedVersion; info:eu-repo/semantics/article Formato: application/pdf
Publicado em /04/2010 Português
Relevância na Pesquisa
66.16%
This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time–frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.; This work has been funded by the Spain CICYT grant TEC2008-02473.; 10 pages, 6 figures.-- PMID: 20217264.

Classification and feature extraction in man and machine; Klassifikation und Merkmalsextraktion in Mensch und Maschine

Graf, Arnulf B. A.
Fonte: Universität Tübingen Publicador: Universität Tübingen
Tipo: Dissertation; info:eu-repo/semantics/doctoralThesis
Português
Relevância na Pesquisa
66.33%
Diese Dissertation befasst sich mit den Mechanismen, die Menschen verwenden, um Merkmale aus visuellen Reizen zu erzeugen und anschliessend zu klassifizieren. Es wird eine experimentelle Methode entwickelt, die menschliche Psychophysik mit maschinellem Lernen verbindet. Im Mittelpunkt der Arbeit steht ein Geschlechtsklassifikationsexperiment, das mit Hilfe der Kopfdatenbank des Max Planck Instituts durchgeführt wird. Hierzu werden verschiedene niedrig-dimensionale Merkmale aus den Gesichtsbildern extrahiert. Das Klassifikationsverfahren auf diesen Merkmalen ist durch eine Trennebene zwischen den beiden Klassen modelliert. Die Antworten der Versuchspersonen werden verglichen und korreliert mit der Distanz der Merkmale zur Trennebene. In dieser Arbeit wird bewiesen, dass maschinelles Lernen ein neues und wirksames algorithmisches Verfahren ist, um Einblicke in menschliche kognitive Prozesse zu erhalten. In einem ersten psychophysischen Klassifikationsexperiment wird gezeigt, dass eine hohe Fehlerrate und ein niedriges Vertrauen der Versuchspersonen einer längeren Verarbeitung der Information im Gehirn entsprechen. Ein zweites Klassifikationsexperiment auf den selben Reizen aber in unterschiedlicher Reihenfolge, bestätigt die Konsistenz der Antworten der Versuchspersonen und die Reproduzierbarkeit der folgenden Resultate. Es wird gezeigt...

Feature Extraction Workflows for Urban Mobile-Terrestrial LiDAR Data

MCQUAT, Gregory John
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
66.28%
Mobile Terrestrial LiDAR (MTL) is an active remote sensing technology that uses laser-based ranging and global positioning systems (GPS) to record 3D point location measurements on surfaces within and near transportation corridors, such as along a railroad track or a street. This thesis examines geovisualization for improving user-oriented workflows and also examines geographic object-based image analysis (GEOBIA) for the development of automated feature extraction. A LiDAR sensor-centric perspective during the data acquisition phase is used to organize data for the user and to transform the data into a 2D reference frame for object-oriented image analysis of MTL data. Organizing the display of MTL data relative to the scanner presented new opportunities for visualization techniques and was an effective method for communicating space that was scanned, or not, in an urban scene. It offers new avenues for quality assessment of MTL survey of urban environments by explicitly displaying gaps in data coverage. A number of techniques for navigating and visualizing data from a sensor-perspective are examined. A novel sensor-perspective transformation of MTL data from three to two dimensions enables analysis of MTL data in common GIS and image-processing environments. GEOBIA software (Definiens’ eCognition) is used to construct a procedural feature extraction workflow. The procedures are constructed with semantic classes...

Feature Extraction Using Sequential Semidefinite Programming

Shen, Chunhua; Li, Hongdong; Brooks, Michael
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Português
Relevância na Pesquisa
66.16%
Many feature extraction approaches end up with a trace quotient formulation. Since it is difficult to directly solve the trace quotient problem, conventionally the trace quotient cost is replaced by an approximation such that the generalised eigen-decompo

Automatic Detection of Facial Midline And Its Contributions To Facial Feature Extraction

Nakao, Nozomi; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2007 Português
Relevância na Pesquisa
56.2%
We propose a novel approach for detection of the facial midline from a frontal face image. Using midline as a guide reduces computational cost required for facial feature extraction (FFE) because the midline is capable of restricting multi-dimensional searching process into one-dimensional search. The proposed method detects the facial midline from an edge image as the symmetry axis using the generalized Hough transformation. Experimental results on the FERET database indicate that the proposed algorithm can accurately detect facial midlines over many different scales and rotation. The total computational time for facial feature extraction has been reduced by a factor of 280 using the midline detected by this method.

Ultrasound speckle feature extraction for scattering structure characterization

Rao, Navalgund; Venkatraman, Shyam; Zhang, Yimou
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Proceedings Formato: 260247 bytes; application/pdf
Português
Relevância na Pesquisa
66.16%
There has been a renewed interest in recent years aimed at understanding the relationship between the various different moments of the ultrasound echo signal and the scattering microstructure. This paper considers the variation of second normalized intensity moments with probe pulse bandwidth theoretically and experimentally. Slope and intercept on this graph are shown to be useful features for the microstructure characterization.; "Ultrasound speckle feature extraction for scattering structure characterization," Proceedings of the IEEE 17th Conference: Engineering in Medicine and Biology Society. Institute of Electrical and Electronics Engineers. Held in Montreal, Quebec, Canada: 20-23 September 1995. ©1995 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases...

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, Sebastian; Raetsch, Gunnar; Weston, Jason; Schoelkopf, Bernhard; Smola, Alexander; Mueller, Klaus-Robert
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
56.2%
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Raylelgh coefficient, we propose nonlinear generalizations of Fisher's discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

TERRAIN-BASED NAVIGATION: A TOOL TO IMPROVE NAVIGATION AND FEATURE EXTRACTION PERFORMANCE OF MOBILE MAPPING SYSTEMS

TOTH, C.; UFPR; GREJNER-BRZEZINSKA, D.A.; OH, J.H.; MARKIEL, J. N.
Fonte: Universidade Federal do Paraná-UFPR Publicador: Universidade Federal do Paraná-UFPR
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; Artigo Avaliado pelos Pares Formato: application/pdf
Publicado em 11/03/2010 Português
Relevância na Pesquisa
56.16%
Terrain-referenced navigation  (TRN) techniques are of increasing interest in the research community, as they can provide alternative navigation tools when GPS is not available or the GPS signals are jammed. Some form of augmentation to cope with the lack of GPS signals is typically required in mobile mapping applications in urban canyons and is of interest for military applications. TRN could provide alternative position and attitude fixes to support an inertial navigation system, since such systems inevitably drift over time if not calibrated by GPS or other methodologies. With improving imaging sensor performance as well as growing worldwide availability of terrain high-resolution data and city models, terrain-based navigation is becoming a viable option to support navigation in GPS-denied environments. Furthermore, the feedback from the imaging sensors can be used even during GPS availability, which increases the redundancy of the measurement update step of the navigation filter, enabling more reliable integrity monitoring at this stage. The relevance of TRN to mobile mapping applications is twofold: (1) the process of obtaining real-time position and attitude fixes for the navigation filter is based on feature extraction...

Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction

Azeem,A.; Sharif,M.; Shah,J.H.; Raza,M.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2015 Português
Relevância na Pesquisa
56.2%
Feature transformation and key-point identification is the solution to many local feature descriptors. One among such descriptor is the Scale Invariant Feature Transform (SIFT). A small effort has been made for designing a hexagonal sampled SIFT feature descriptor with its applicability in face recognition tasks. Instead of using SIFT on square image coordinates, the proposed work makes use of hexagonal converted image pixels and processing is applied on hexagonal coordinate system. The reason of using the hexagonal image coordinates is that it gives sharp edge response and highlights low contrast regions on the face. This characteristic allows SIFT descriptor to mark distinctive facial features, which were previously discarded by original SIFT descriptor. Furthermore, Fisher Canonical Correlation Analysis based discriminate procedure is outlined to give a more precise classification results. Experiments performed on renowned datasets revealed better performances in terms of feature extraction in robust conditions.