Search results for "Ground truth"
showing 10 items of 59 documents
Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Preliminary results
2015
Spine is a structure commonly involved in several prevalent diseases. In clinical diagnosis, therapy, and surgical intervention, the identification and segmentation of the vertebral bodies are crucial steps. However, automatic and detailed segmentation of vertebrae is a challenging task, especially due to the proximity of the vertebrae to the corresponding ribs and other structures such as blood vessels. In this study, to overcome these problems, a probabilistic atlas of the spine, including cervical, thoracic and lumbar vertebrae has been built to introduce anatomical knowledge in the segmentation process, aiming to deal with overlapping gray levels and the proximity to other structures. F…
Weakly supervised alignment of multisensor images
2015
Manifold alignment has become very popular in recent literature. Aligning data distributions prior to product generation is an appealing strategy, since it allows to provide data spaces that are more similar to each other, regardless of the subsequent use of the transformed data. We propose a methodology that finds a common representation among data spaces from different sensors using geographic image correspondences, or semantic ties. To cope with the strong deformations between the data spaces considered, we propose to add nonlineari-ties by expanding the input space with Gaussian Radial Basis Function (RBF) features with respect to the centroids of a partitioning of the data. Such featur…
Stereo Matching Tecniques for Cloud-top Height Retrieval
2006
This paper presents an ongoing study for the estimation of the cloud-top height by using only geometrical methods. It is based on the hypothesis that an infra-red camera is on board a satellite and pairs of images concern nearly the same scene. Stereo-vision techniques are therefore explored in order to test the methodology for height retrieval and in particular results of several techniques of stereo matching are evaluated. This study includes area-based matching algorithms by implementing the basic versions, without considering any further steps of optimisation to improve the results. Dense depth maps are the final outputs whose reliability is verified by computing error statistics with r…
Modelling of ERS-2 radar backscattering over rocky arid regions
2003
This paper discusses the potential of radar signals to characterize stone cover over bare soil surfaces in arid rocky regions. Ground truth and radar measurements were made over two arid sites: Avdat (Israel) and Foumtillich (Morocco). An empirical approach is proposed to derive stone cover percentage from pin profiler measurements. A numerical Moment method based on integral equations of backscattering has been used to study the influence of stone cover on simulated radar signal. The results of the simulations are compared with European Remote Sensing Satellite (ERS-2) radar measurements over test areas with different stone covers. Excellent agreement is observed between numerical simulati…
Phase retrieval of a Kolmogorov phase screen from very sparse data using four binary masks
2020
We investigate experimentally the phase retrieval of a Kolmogorov phase screen from very sparse data by modulating its amplitude with four binary masks and compare the retrieved phase screen to the ground truth measured with a surface profiler. Previously, we have shown in simulations that this kind of modulation can be successfully used for the phase retrieval of a Kolmogorov phase screen. After subtracting the ground truth from the retrieved phase screen, the root-mean-square error decreased from 0.14 µm to 0.10 µm. We conclude that a Kolmogorov phase screen can be recovered using simple modulation and very sparse data.
On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth
2015
In many applications, data from different sensors are aggregated in order to obtain more reliable information about the process that the sensors are monitoring. However, the quality of the aggregated information is intricately dependent on the reliability of the individual sensors. In fact, unreliable sensors will tend to report erroneous values of the ground truth, and thus degrade the quality of the fused information. Finding strategies to identify unreliable sensors can assist in having a counter-effect on their respective detrimental influences on the fusion process, and this has has been a focal concern in the literature. The purpose of this paper is to propose a solution to an extreme…
On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments.
2017
The purpose of this paper is to propose a solution to an extremely pertinent problem, namely, that of identifying unreliable sensors (in a domain of reliable and unreliable ones) without any knowledge of the ground truth. This fascinating paradox can be formulated in simple terms as trying to identify stochastic liars without any additional information about the truth. Though apparently impossible, we will show that it is feasible to solve the problem, a claim that is counterintuitive in and of itself. One aspect of our contribution is to show how redundancy can be introduced, and how it can be effectively utilized in resolving this paradox. Legacy work and the reported literature (for exam…
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…
Semi-automatic registration of retinal images based on line matching approach
2013
Accurate retinal image registration is essential to track the evolution of eye-related diseases. We propose a semiautomatic method based on features relying upon retinal graphs for temporal registration of retinal images. The features represent straight lines connecting vascular landmarks on the retina vascular tree: bifurcations, branchings, crossings, end points. In the built retinal graph, one straight line between two vascular landmarks indicates that they are connected by a vascular segment in the original retinal image. The locations of the landmarks are manually extracted to avoid the information loss due to errors in a retinal vessels segmentation algorithms. A straight line model i…