Spelling suggestions: "subject:"chap:template_matching"" "subject:"gatewatching""
31 |
Optimizing Multi-Station Earthquake Template Matching Through Re-Examination of the Youngstown, Ohio SequenceSkoumal, Robert J. 13 May 2014 (has links)
No description available.
|
32 |
Re-evaluation of the 2009-2011 Southern Fort-Worth Basin (TX) Earthquakes: Potential Relationships with Hydraulic Fracturing and Wastewater InjectionSmith, Sarah L R 02 August 2017 (has links)
No description available.
|
33 |
Histogram-based template matching object detection in images with varying brightness and contrastSchrider, Christina Da-Wann 16 October 2008 (has links)
No description available.
|
34 |
Characterizing induced and natural earthquake swarms using correlation algorithmsSkoumal, Robert J. 13 April 2016 (has links)
No description available.
|
35 |
Design of Keyword Spotting System Based on Segmental Time Warping of Quantized FeaturesKarmacharya, Piush January 2012 (has links)
Keyword Spotting in general means identifying a keyword in a verbal or written document. In this research a novel approach in designing a simple spoken Keyword Spotting/Recognition system based on Template Matching is proposed, which is different from the Hidden Markov Model based systems that are most widely used today. The system can be used equally efficiently on any language as it does not rely on an underlying language model or grammatical constraints. The proposed method for keyword spotting is based on a modified version of classical Dynamic Time Warping which has been a primary method for measuring the similarity between two sequences varying in time. For processing, a speech signal is divided into small stationary frames. Each frame is represented in terms of a quantized feature vector. Both the keyword and the speech utterance are represented in terms of 1‐dimensional codebook indices. The utterance is divided into segments and the warped distance is computed for each segment and compared against the test keyword. A distortion score for each segment is computed as likelihood measure of the keyword. The proposed algorithm is designed to take advantage of multiple instances of test keyword (if available) by merging the score for all keywords used. The training method for the proposed system is completely unsupervised, i.e., it requires neither a language model nor phoneme model for keyword spotting. Prior unsupervised training algorithms were based on computing Gaussian Posteriorgrams making the training process complex but the proposed algorithm requires minimal training data and the system can also be trained to perform on a different environment (language, noise level, recording medium etc.) by re‐training the original cluster on additional data. Techniques for designing a model keyword from multiple instances of the test keyword are discussed. System performance over variations of different parameters like number of clusters, number of instance of keyword available, etc were studied in order to optimize the speed and accuracy of the system. The system performance was evaluated for fourteen different keywords from the Call - Home and the Switchboard speech corpus. Results varied for different keywords and a maximum accuracy of 90% was obtained which is comparable to other methods using the same time warping algorithms on Gaussian Posteriorgrams. Results are compared for different parameters variation with suggestion of possible improvements. / Electrical and Computer Engineering
|
36 |
Wiedererkennung ungefilterter und Fourier-gefilterter Schwarzweißmuster duch Honigbienen (Apis mellifera L.)Efler, Daniela Margarete 02 July 2004 (has links)
Honigbienen (Apis mellifera L.) sind in der Lage mit ihren Komplexaugen visuelle Muster wahrzunehmen und die Musterinformation im Zentralen Nervensystem zu speichern und für Ähnlichkeitsbewertungen wieder abzurufen. Die vorliegende Arbeit zeigt klare Evidenz gegen eine ausschließliche Bewertung von Schwarzweißmustern mit Hilfe von Template-Matching-Mechanismen. Mit systematisch abgewandelten Dressurparadigmen trainierte Bienen bewerteten Muster unabhängig von der erfolgten Dressur stets bevorzugt gemäß eher grober Mustereigenschaften, wie zum Beispiel die Parameter "schwarzer Musterzentralbereich" und "Musterzerstreutheit". Veränderte man in einem weiteren Versuchansatz die Musterinformation der Schwarzweißmuster zudem gezielt durch geeignete Fourier-Filterung, zeigte sich, dass Bienen zur Musterdiskriminierung bereits die Frequenzinformation von 2 - 8 Schwingungen/Bildbreite genügte. Diese Unschärfe der bewerteten Bildinformation ließ sich nicht ausschließlich aus den optischen Eigenschaften des visuellen Apparates der Bienen ableiten. Videodokumentationen und Einzelbildanalyse des Flugverhaltens der Bienen vor den Mustern ergaben zudem keinerlei Hinweise für eine Nutzung des Flugverhaltens als Bewertungsgrundlage zur Musterdiskriminierung. Die erhaltenen Ergebnisse zur Musterdiskriminierung wurden vor dem Hintergrund eines ökonomischen Entscheidungsmodells für menschliches Verhalten, den Frugalheuristiken, diskutiert und Hinweise auf eine ökonomische Bewertungsstrategie der Bienen entsprechend einer Take-The-Best-Heuristik gefunden. / Honeybees (Apis mellifera L.) are able to perceive visual patterns through their compound eyes and store the visual information in the central nervous system for subsequent use in pattern discrimination tasks. This thesis provides clear evidence against the assumption that pattern discrimination relies exclusively on template matching mechanisms. Bees discriminated pairs of patterns preferential using extracted pattern parameters. Within this thesis the preferred parameters of the bees following the training paradigms were coarse parameters such as "black centre" and "pattern disruption". In experiments with Fourier filtered patterns the frequency information of the patterns were additionally reduced. The results showed that bees could discriminate patterns using only 2 - 8 cycles/pattern-width of the frequency information. The fuzziness of the exploited visual information could not be assigned to restrictions of the visual system of bees. Additional documentation and single picture analysis of the videotaped flight behaviour in front of the patterns provided no evidence for bees using their flight behaviour in order to enhance the pattern discrimination ability. Application of economic human decision models (frugal heuristics) to the behavioural results showed clues that bees'' decisions could be explained with the help of the Take-The-Best-heuristic.
|
37 |
DETECÇÃO DE MASSAS EM IMAGENS MAMOGRÁFICAS USANDO ÍNDICE DE DIVERSIDADE DE SIMPSON E MÁQUINA DE VETORES DE SUPORTE. / Mass detection in mammography images using SIMPSON's diversity index and vectoring machine support.NUNES, André Pereira 20 February 2009 (has links)
Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-21T14:59:23Z
No. of bitstreams: 1
Andre Pereira.pdf: 3105574 bytes, checksum: 06e2fe68d48179a3c62a46e447b82513 (MD5) / Made available in DSpace on 2017-08-21T14:59:23Z (GMT). No. of bitstreams: 1
Andre Pereira.pdf: 3105574 bytes, checksum: 06e2fe68d48179a3c62a46e447b82513 (MD5)
Previous issue date: 2009-02-20 / Breast cancer is one of the major causes of mortality among women throughout
the world. Presently, the analysis of breast radiography is the most used
method to early detection of this kind of cancer. It enables the identification of
anomalies at their initial stage, which is a fundamental factor for success in the
treatment. The sensitivity of this kind of exam, although, depends on several
factors, such as the size and the location of the abnormalities, density of the
breast tissue, quality of the technical resources and radiologist's ability. This
work presents a methodology that uses the K-Means clustering algorithm and
the Template Matching technique for segmentation of suspicious regions. Next,
geometry and texture features are extracted from each of these regions, being
the texture described by the Simpson's Diversity Index, a statistic used in
Ecology to measure the biodiversity of an ecosystem. Finally, this information is
submitted to a Support Vector Machine so that the suspicious regions are
classified into masses and non-masses. The methodology was tested with 650
mammographic images from the DDSM database, achieving 83.94% of
accuracy, 83.24% of sensibility and 84.14% of specificity in average. / O câncer de mama é uma das maiores causas de mortalidade entre as
mulheres no mundo todo. Atualmente, a análise da radiografia da mama é o
recurso mais utilizado na detecção precoce desse tipo de câncer, pois
possibilita a identificação de anomalias em sua fase inicial, fator fundamental
para o sucesso do tratamento. A sensibilidade desse tipo de exame, no
entanto, depende de diversos fatores, tais como tamanho e localização das
anomalias, densidade do tecido mamário, qualidade dos recursos técnicos e
habilidade do radiologista. Este trabalho apresenta uma metodologia para
detecção de massas em imagens digitais de mamografias que poderá auxiliar o
especialista em sua análise. O método proposto utiliza o algoritmo de
agrupamento K-Means e a técnica de Template Matching para segmentar as
regiões suspeitas de conterem massas. Em seguida, medidas de geometria e
textura são extraídas de cada uma dessas regiões, sendo a textura descrita
através do Índice de Diversidade de Simpson, uma estatística usada na
Ecologia para mensurar a biodiversidade de um ecossistema. Finalmente,
essas informações são submetidas a uma Máquina de Vetores de Suporte para
que as regiões suspeitas sejam classificadas em massas ou não massas. A
metodologia foi testada com 650 imagens mamográficas obtidas da base de
dados DDSM, atingindo 83,94% de acurácia, 83,24% de sensibilidade, e
84,14% de especificidade em média.
|
38 |
Casamento de padrões em imagens digitais livre de segmentação e invariante sob transformações de similaridade. / Segmentation-free template matching in digital images invariant to similarity transformations.Araújo, Sidnei Alves de 21 October 2009 (has links)
Reconhecimento de padrões em imagens é um problema clássico da área de visão computacional e consiste em detectar um padrão ou objeto de referência (template) em uma imagem digital. A maioria dos métodos para esta finalidade propostos na literatura simplifica as imagens por meio de operações como binarização, segmentação e detecção de bordas ou pontos de contorno, para em seguida extrair um conjunto de atributos descritores. O problema é que esta simplificação pode descartar informações importantes para descrição dos padrões, fazendo diminuir a robustez do processo de detecção. Um método eficiente deve ter a habilidade de identificar um padrão sujeito a algumas transformações geométricas como rotação, escalonamento, translação, cisalhamento e, no caso de métodos para imagens coloridas, deve ainda tratar do problema da constância da cor. Além disso, o conjunto de atributos que descrevem um padrão deve ser pequeno o suficiente para viabilizar o desenvolvimento de aplicações práticas como um sistema de visão robótica ou um sistema de vigilância. Estes são alguns dos motivos que justificam os esforços empreendidos nos inúmeros trabalhos desta natureza encontrados na literatura. Neste trabalho é proposto um método de casamento de padrões em imagens digitais, denominado Ciratefi (Circular, Radial and Template-Matching Filter), livre de segmentação e invariante sob transformações de similaridade, brilho e contraste. O Ciratefi consiste de três etapas de filtragem que sucessivamente descartam pontos na imagem analisada que não correspondem ao padrão procurado. Também foram propostas duas extensões do Ciratefi, uma que utiliza operadores morfológicos na extração dos atributos descritores, denominada Ciratefi Morfológico e outra para imagens coloridas chamada de color Ciratefi. Foram realizados vários experimentos com o intuito de comparar o desempenho do método proposto com dois dos principais métodos encontrados na literatura. Os resultados experimentais mostram que o desempenho do Ciratefi é superior ao desempenho dos métodos empregados na análise comparativa. / Pattern recognition in images is a classical problem in computer vision. It consists in detecting some reference pattern or template in a digital image. Most of the existing pattern recognition techniques usually apply simplifications like binarization, segmentation, interest points or edges detection before extracting features from images. Unfortunately, these simplification operations can discard rich grayscale information used to describe the patterns, decreasing the robustness of the detection process. An efficient method should be able to identify a pattern subject to some geometric transformations such as translation, scale, rotation, shearing and, in the case of color images, should deal with the color constancy problem. In addition, the set of features that describe a pattern should be sufficiently small to make feasible practical applications such as robot vision or surveillance system. These are some of the reasons that justify the effort for development of many works of this nature found in the literature. In this work we propose a segmentation-free template matching method named Ciratefi (Circular, Radial and Template-Matching Filter) that is invariant to rotation, scale, translation, brightness and contrast. Ciratefi consists of three cascaded filters that successively exclude pixels that have no chance of matching the template from further processing. Also we propose two extensions of Ciratefi, one using the mathematical morphology approach to extract the descriptors named Morphological Ciratefi and another to deal with color images named Color Ciratefi. We conducted various experiments aiming to compare the performance of the proposed method with two other methods found in the literature. The experimental results show that Ciratefi outperforms the methods used in the comparison analysis.
|
39 |
Casamento de padrões em imagens digitais livre de segmentação e invariante sob transformações de similaridade. / Segmentation-free template matching in digital images invariant to similarity transformations.Sidnei Alves de Araújo 21 October 2009 (has links)
Reconhecimento de padrões em imagens é um problema clássico da área de visão computacional e consiste em detectar um padrão ou objeto de referência (template) em uma imagem digital. A maioria dos métodos para esta finalidade propostos na literatura simplifica as imagens por meio de operações como binarização, segmentação e detecção de bordas ou pontos de contorno, para em seguida extrair um conjunto de atributos descritores. O problema é que esta simplificação pode descartar informações importantes para descrição dos padrões, fazendo diminuir a robustez do processo de detecção. Um método eficiente deve ter a habilidade de identificar um padrão sujeito a algumas transformações geométricas como rotação, escalonamento, translação, cisalhamento e, no caso de métodos para imagens coloridas, deve ainda tratar do problema da constância da cor. Além disso, o conjunto de atributos que descrevem um padrão deve ser pequeno o suficiente para viabilizar o desenvolvimento de aplicações práticas como um sistema de visão robótica ou um sistema de vigilância. Estes são alguns dos motivos que justificam os esforços empreendidos nos inúmeros trabalhos desta natureza encontrados na literatura. Neste trabalho é proposto um método de casamento de padrões em imagens digitais, denominado Ciratefi (Circular, Radial and Template-Matching Filter), livre de segmentação e invariante sob transformações de similaridade, brilho e contraste. O Ciratefi consiste de três etapas de filtragem que sucessivamente descartam pontos na imagem analisada que não correspondem ao padrão procurado. Também foram propostas duas extensões do Ciratefi, uma que utiliza operadores morfológicos na extração dos atributos descritores, denominada Ciratefi Morfológico e outra para imagens coloridas chamada de color Ciratefi. Foram realizados vários experimentos com o intuito de comparar o desempenho do método proposto com dois dos principais métodos encontrados na literatura. Os resultados experimentais mostram que o desempenho do Ciratefi é superior ao desempenho dos métodos empregados na análise comparativa. / Pattern recognition in images is a classical problem in computer vision. It consists in detecting some reference pattern or template in a digital image. Most of the existing pattern recognition techniques usually apply simplifications like binarization, segmentation, interest points or edges detection before extracting features from images. Unfortunately, these simplification operations can discard rich grayscale information used to describe the patterns, decreasing the robustness of the detection process. An efficient method should be able to identify a pattern subject to some geometric transformations such as translation, scale, rotation, shearing and, in the case of color images, should deal with the color constancy problem. In addition, the set of features that describe a pattern should be sufficiently small to make feasible practical applications such as robot vision or surveillance system. These are some of the reasons that justify the effort for development of many works of this nature found in the literature. In this work we propose a segmentation-free template matching method named Ciratefi (Circular, Radial and Template-Matching Filter) that is invariant to rotation, scale, translation, brightness and contrast. Ciratefi consists of three cascaded filters that successively exclude pixels that have no chance of matching the template from further processing. Also we propose two extensions of Ciratefi, one using the mathematical morphology approach to extract the descriptors named Morphological Ciratefi and another to deal with color images named Color Ciratefi. We conducted various experiments aiming to compare the performance of the proposed method with two other methods found in the literature. The experimental results show that Ciratefi outperforms the methods used in the comparison analysis.
|
40 |
Fast Object Recognition in Noisy Images Using Simulated AnnealingBetke, Margrit, Makris, Nicholas 25 January 1995 (has links)
A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.
|
Page generated in 0.0558 seconds