Search results for "ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION"
showing 10 items of 982 documents
Based on Compton Camera
2016
Compton Cameras have been proposed as an alternative to SPECT imaging with Gamma Camera, mainly due to factors such as the electronic collimation, which allows a bigger field of view and provides further information from the acquired events if compared to devices with mechanical collimation. By contrast, this involves a higher amount of data to be processed. In medical devices this leads to waiting times that are generally higher than desirable by the health-care professionals. In this work we have assessed the reconstruction of Compton images by making use of iterative and non-iterative techniques, and also evaluated its performances as a SPECT imaging technique.
Compressive single-pixel multispectral Stokes polarimeter
2014
We present a single-pixel system that performs polarimetric multispectral imaging with the aid of compressive sensing techniques. We experimentally obtain the full Stokes spatial distribution of a scene for different spectral channels.
The on-board calibration system of the X-ray Imaging Polarimetry Explorer (XIPE)
2016
The calibration system for XIPE is aimed at providing a way to check and correct possible variations of performance of the Gas Pixel Detector during the three years of operation in orbit (plus two years of possible extended operation), while facilitating the observation of the celestial sources. This will be performed by using a filter wheel with a large heritage having a set of positions for the calibration and the observation systems. In particular, it will allow for correcting possible gain variation, for measuring the modulation factor using a polarized source, for removing non interesting bright sources in the field of view and for observing very bright celestial sources. The on-board …
Color Image Segmentation: The Hypergraph Framework
2006
International audience; Color Image Segmentation: The Hypergraph Framework
Eye movements when reading sentences with handwritten words.
2016
The examination of how we read handwritten words (i.e., the original form of writing) has typically been disregarded in the literature on reading. Previous research using word recognition tasks has shown that lexical effects (e.g., the word-frequency effect) are magnified when reading difficult handwritten words. To examine this issue in a more ecological scenario, we registered the participants’ eye movements when reading handwritten sentences that varied in the degree of legibility (i.e., sentences composed of words in easy vs. difficult handwritten style). For comparison purposes, we included a condition with printed sentences. Results showed a larger reading cost for sentences with dif…
Removal of streaking artefact in the images of the Pierre Auger Observatory infra-red cameras
2011
In this paper the problem of removing concentric semi-circular stripe artefacts induced by the operating hardware of the infra red cameras of the Pierre Auger Observatory, is tackled. The method builds on top of a recent algorithm for the removal of artefacts, which presents a robust filter, obtained as a combination of Wavelet and Fourier analysis, capable of eliminating horizontal and vertical stripes in images, while trying to preserve structural features and quantitative values of the image. The method requires several parameters which have been tuned by an exhaustive test on a large set of images. The results show that the method is capable to satisfactorily remove the stripe artefacts.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
2016
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…
Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR
2021
In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…
Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images
2020
The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter…
Space variant vision and pipelined architecture for time to impact computation
2002
Image analysis is one of the most interesting ways for a mobile vehicle to understand its environment. One of the tasks of an autonomous vehicle is to get accurate information of what it has in front, to avoid collision or find a way to a target. This task requires real-time restrictions depending on the vehicle speed and external object movement. The use of normal cameras, with homogeneous (squared) pixel distribution, for real-time image processing, usually requires high performance computing and high image rates. A different approach makes use of a CMOS space-variant camera that yields a high frame rate with low data bandwidth. The camera also performs the log-polar transform, simplifyin…