Search results for "PIXE"
showing 10 items of 428 documents
Three-Dimensional Separation and Characterization of Fractures in X-Ray Computed Tomographic Images of Rocks
2020
Open fractures can affect petrophysical properties of their host rock masses, as well as fluid transport and storage, so characterization of them is important to both industrial and research scientists. X-ray Computed Tomography (CT), a non-destructive technique for 3D imaging of various materials, shows such fractures well in rock samples. However, separation and characterization of fractures in CT data is complicated when a scanned sample contains narrow and intersecting fractures, because narrow fractures become blurred when thinner than the scanner resolution and their value approximates the one of the matrix, and because intersecting features are difficult to individually characterize.…
Thermoelastic stress analysis by means of an infrared scanner and a two-dimensional fast Fourier transform-based lock-in technique
2008
An infrared thermographic experimental set-up has been proposed and evaluated towards the capability to measure thermoelastic-effect-induced temperature changes. A standard infrared thermocamera with a nominal noise-equivalent temperature difference (NETD) resolution of 0.12 K has been employed to measure the temperature from unidirectional glass-reinforced plastic tensile coupons under cyclic sinusoidal loads. The raster scanning mode of the camera single detector produces a time delay in acquiring the signal from two succeeding pixels on the same row, and from consecutive scanned rows. By exploiting the acquired dwell times, it was possible to produce a periodic pattern on the thermal ma…
Use of balanced detection in single-pixel imaging
2016
We introduce balanced detection in combination with simultaneous complementary illumination in a single-pixel architecture. With this novel detection scheme we are able to recover a real-time video stream in presence of ambient light.
Prototyping algorithm for retrieving FAPAR using MSG data in the context of the LSA SAF project
2007
This paper describes the prototyping algorithm developed for retrieving the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using MSG data in the framework of satellite application facility on land surface analysis (LSA SAF). The prototyping relies on the Roujean and Breon (1995) method, which is based on simulations of visible and near infrared reflectance values in an optimal geometry. A relationship is found between a vegetation index and daily FAPAR The algorithm has been applied to one year of MSG BRDF data since August 2005, using a temporal frequency of 5-days, and then validated against a set of operational satellite FAPAR products such as MODIS, MERIS, SeaWiFS and …
Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging
2020
Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…
Using active learning to adapt remote sensing image classifiers
2011
The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and cluster…
Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators
2016
Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…
Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the DRIVE database shows accurate ex…
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…
Computation and Display of 3D Movie From a Single Integral Photography
2016
Integral photography is an auto-stereoscopic technique that allows, among other interesting applications, the display of 3D images with full parallax and avoids the painful effects of the accommodation-convergence conflict. Currently, one of the main drawbacks of this technology is the need of a huge amount of data, which have to be stored and transmitted. This is due to the fact that behind every visual resolution unit, i.e. behind any microlens of an integral-photography monitor, between 100 and 300 pixels should appear. In this paper, we make use of an updated version of our algorithm, SPOC 2.0, to alleviate this situation. We propose the application of SPOC 2.0 for the calculation of co…