Herramienta software para inspección termográfica usando fusión multimodal de imágenes térmicas y de espectro visible

Ospina, Rafael E.; Cardona, Sergio D.; Bacca-Cortes, Bladimir

doi:10.25100/iyc.v19i1.2130

Cited by 3 publications

(2 citation statements)

References 9 publications

(20 reference statements)

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“…Posteriormente, la aplicación TermoIR [18] con un mayor enfoque a TNDT permite la adquisición, procesamiento, análisis y caracterización de información térmica. Por último, en [28], se presenta un desarrollo software para fusión multimodal de imágenes térmicas y de espectro visible.…”

Section: A Técnicas De Procesamientounclassified

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

2022

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En este artículo se sintetiza una revisión de los principales trabajos relacionados con ensayo no destructivo usando termografía infrarroja incluyendo aportes realizados por investigadores de América Latina. Se encontró en este campo una tendencia creciente de la producción académica en los últimos años, lo cual ratifica que es un área de interés y desarrollo continuo. El resultado de la revisión se clasificó en cuatro líneas temáticas cubriendo principalmente: i) trabajos que recopilan el estado del arte, ii) desarrollos en técnicas de procesamiento de información térmica, iii) indicadores de desempeño y iv) simulación, modelos térmicos y campos de aplicación. Adicionalmente, se registraron los países tradicionales y emergentes que están generando contribuciones en el sector del ensayo no destructivo usando termografía infrarroja. Finalmente, se identificó cómo el enfoque actual del procesamiento de información térmica está orientado al procesamiento de secuencias de imágenes, donde el número de técnicas es cada vez mayor y se evidenció una reducida cantidad de métricas para la evaluación objetiva de su rendimiento

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Section: A Técnicas De Procesamientounclassified

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

2022

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“…Several researchers have implemented medical image fusion using different imaging modalities based on a wavelet-based approach 31 , 32 . Ospina et al developed a software tool for fusing infrared and visible images based on multimodal fusion technique using a hybrid camera system called INVI (Infrared and visual spectrum images) fusion 1.0 33 . The software contains the following features: (1) the intrinsic and extrinsic camera calibration using 2D homography; (2) used in-built automated tool for enhancing thermal and digital images; (3) fusion of infrared and visible facial and machinery images.…”

Section: Introductionmentioning

confidence: 99%

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

Thirunavukkarasu,

Umapathy,

Ravi

et al. 2024

Sci Rep

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The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants. The visible and thermal tongue images are acquired using digital single lens reference camera and thermal infrared cameras, respectively. The digital and thermal tongue images are fused based on the wavelet transform method. Then Gray level co-occurrence matrix features are extracted individually from the visible, thermal, and fused tongue images. The machine learning classifiers and deep learning networks such as VGG16 and ResNet50 was used to classify the normal and diabetes mellitus. Image quality metrics are implemented to compare the classifiers’ performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% and 85% before fusion of individual thermal and visible tongue images, respectively. Therefore, this study results indicates that fused tongue images might be used as a non-contact elemental tool for pre-screening type II diabetes mellitus.

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Software Tool for the Extrinsic Calibration of Infrared and RGBD Cameras Applied to Thermographic Inspection

Taimal

Bacca-Cortes

Girón

2022

Ing.

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Context: Thermographic inspections are currently used to assess energy efficiency in electrical equipment and civil structures or to detect failures in cooling systems and electrical or electronic devices. However, thermal images lack texture details, which does not allow for a precise identification of the geometry of the scene or the objects in it. Method: In this work, the development of the software tool called DepTherm is described. This tool allows performing intrinsic and extrinsic calibration between infrared and RGBD cameras in order to fuse thermal, RGB, and RGBD images, as well as to record thermal and depth data. Additional features include user management, a visualization GUI for all three types of images, database storage, and report generation. Results: In addition to the integration tests performed to validate the functionality of DepTherm, two quantitative tests were conducted in order to evaluate its accuracy. A maximum re-projection error of 1,47±0,64 pixels was found, and the maximum mean error in registering an 11 cm side cube was 4,15 mm. Conclusions: The features of the DepTherm software tool are focused on facilitating thermographic inspections by capturing 3D scene models with thermal data.

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Herramienta software para inspección termográfica usando fusión multimodal de imágenes térmicas y de espectro visible

Cited by 3 publications

References 9 publications

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

Software Tool for the Extrinsic Calibration of Infrared and RGBD Cameras Applied to Thermographic Inspection

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