Termografía infrarroja aplicada en cúpulas históricas: identificación y análisis de sistemas constructivos

El uso específico de la termografía infrarroja como técnica no destructiva permite el estudio de sistemas constructivos en edificios históricos (caracterización de materiales, disposición constructiva o identificación de elementos originales). Así, se muestra una aplicación práctica en templos en la provincia de Alicante construidos entre finales del siglo XVII y principios del xix, donde se analiza la complementariedad de las imágenes termográficas de 140 cúpulas (junto con datos recopilados in situ, dibujos originales y levantamiento de planos) como herramienta de estudio en fases de análisis previas a una restauración. En conclusión, la investigación detalla una aplicación termográfica al estudio de distintos aspectos como el reconocimiento de materiales empleados (90,71 % ladrillo macizo, 6,43 % ladrillo hueco y 2,86 % piedra), variaciones de espesor interior y exterior (75,71 % perfil apuntado, 17,86 % perfil semiesférico y 6,43 % perfil rebajado), caracterización de grietas/fisuras (estado de conservación) e identificación de cúpulas originales (96,43 %) o restauradas (3,57 %).

Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.