Análisis de resolución en imágenes de fotoelasticidad: caso carga dinámica

León, Juan Diego; Toro, Hermes A. Fandiño; Restrepo-Martínez, Alejandro; Bedoya, John William Branch

doi:10.14483/22484728.12789

Cited by 7 publications

(5 citation statements)

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“…In the case of the color image to determine the similarity with the reference the distance between histograms is used, in the example of the Figure 6 the Bhattacharyya distance [21] for histograms (Eq. ( 41)) was used, instead of Euclidean distance, because a better response was obtained.…”

Section: Color Imagesmentioning

confidence: 99%

Object Recognition and Tracking Using the Particle Estimator

Comas¹,

Stácul²

2022

Digital Image Processing Applications

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In this chapter we describe the particle estimators and its effectiveness for tracking objects in video sequences. The particles estimators are specifically advantageous in transition state models and measurements, especially when these are non-linear and not Gaussian. Once the target object to follow has been identified (in position and size) its main characteristics are obtained using algorithms such as FAST, SURF, BRIEF or ORB. As the particle estimator is a recursive Bayesian estimator, where observations update the probability of validating a hypothesis, that is, they use all the available information to reduce the amount of uncertainty present in an inference or decision problem. Therefore, the main characteristics of the object to follow are those that will determine the probability of validating the hypothesis in the particle estimator. Finally, as an example, the application of a particle estimator is described in a real case of tracking an object in a sequence of infrared images.

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Section: Color Imagesmentioning

confidence: 99%

Object Recognition and Tracking Using the Particle Estimator

Comas¹,

Stácul²

2022

Digital Image Processing Applications

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“…De forma más particular, el método de la fotoelasticidad RGB consiste en determinar el retardo relativo y con él, la distribución de la diferencia de esfuerzos principales, a través de una imagen, obtenida por una cámara digital, del espécimen de un material birrefringente al esfuerzo, en un polariscopio circular [17]. Dicho retardo está en función del campo de esfuerzo inducido, por lo que tendrá características distintas en cada punto de la pieza [1,18]; lo anterior por medio de la relación:…”

Section: Fotoelasticidad Rgbunclassified

Software de fotoelasticidad RGB en placas de materiales birrefringentes al esfuerzo

2021

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La fotoelasticidad digital utiliza el fenómeno de la birrefringencia para el análisis y descripción de la distribución de esfuerzos a través de imágenes digitales y del procesamiento digital de las mismas. En este trabajo se presenta el desarrollo y diseño de un código, abierto y gratuito, en MATLAB que adopta la metodología de la fotoelasticidad RGB para determinar la distribución de la diferencia de esfuerzos principales en una probeta plana de un material fotoelástico, sometida a cargas mecánicas en un estado de esfuerzos planos. Esto mediante el procesamiento de una imagen digital a color de un espécimen de material birrefringente al esfuerzo, colocado dentro de un polariscopio circular en su configuración de campo oscuro. Se presenta la generación de imágenes artificiales representativas del fenómeno con el fin de tener datos de entrada para el desarrollo del algoritmo y una forma de validarlo. Se muestran también los pasos necesarios para la metodología de la fotoelasticidad RGB que incluyen la preparación de una base de datos o tabla de búsqueda y el análisis de la imagen fotoelástica. Se compara, además, el resultado del cálculo de la diferencia de esfuerzos principales con resultados de elemento finito, en lo cual se obtuvo un buen grado de confiabilidad en las imágenes utilizadas.

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“…A possible solution to this complex situation can be developed with the integrated employment of the resources of a Learning Management System (LMS), that which allows to guarantee the transmission of knowledge [4][5][6]. This alternative in occasions is limited by the technical characteristics of the computer services in the university institutions (bandwidth and computation capacity) and the level of pedagogic preparation of the teachers and their experiences in the didactic appropriate employment of the means and available resources.…”

Section: Source: Ownmentioning

confidence: 99%

Biomedical Engineering: experiences in the research formation with MOODLE

et al. 2020

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The work shows the didactic experiences reached in the research formation of biomedical engineers in the Technological University of Havana (Cujae), during the development of courses (Project of Research I and II) in the Biomedical Engineering program, that contribute to the scientific and methodological development of the Work of Diploma like act of culmination of the university studies, with the integrated employment of the resources of a Learning Management System: MOODLE (Forum, Dynamic Presentations, Videos, WIKI, Tasks, Surveys, Questionnaires, etc.) and the Teaching Based in Problems (EBP), to achieve the appropriate motivation and research formation in the students from the curriculum.

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Análisis de resolución en imágenes de fotoelasticidad: caso carga dinámica

Cited by 7 publications

References 0 publications

Object Recognition and Tracking Using the Particle Estimator

Object Recognition and Tracking Using the Particle Estimator

Software de fotoelasticidad RGB en placas de materiales birrefringentes al esfuerzo

Biomedical Engineering: experiences in the research formation with MOODLE

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