Search results for "image processing"
showing 10 items of 3285 documents
Multispectral constancy for illuminant invariant representation of multispectral images
2018
A conventional color imaging system provides high resolution spatial information and low resolution spectral data. In contrast, a multispectral imaging system is able to provide both the spectral and spatial information of a scene in high resolution. A multispectral imaging system is complex and it is not easy to use it as a hand held device for acquisition of data in uncontrolled conditions. The use of multispectral imaging for computer vision applications has started recently but is not very efficient due to these limitations. Therefore, most of the computer vision systems still rely on traditional color imaging and the potential of multispectral imaging for these applications has yet to …
Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality
2014
International audience; Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay diFFerential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We …
The Kolmogorov Spline Network for Image Processing
2011
In 1900, Hilbert stated that high order equations cannot be solved by sums and compositions of bivariate functions. In 1957, Kolmogorov proved this hypothesis wrong and presented his superposition theorem (KST) that allowed for writing every multivariate functions as sums and compositions of univariate functions. Sprecher has proposed in (Sprecher, 1996) and (Sprecher, 1997) an algorithm for exact univariate function reconstruction. Sprecher explicitly describes construction methods for univariate functions and introduces fundamental notions for the theorem comprehension (such as tilage). Köppen has presented applications of this algorithm to image processing in (Köppen, 2002) and (Köppen &…
Deep learning architectures for automatic detection of viable myocardiac segments
2021
Thesis abstract: Deep learning architectures for automatic detection of viable myocardiac segmentsAccurate myocardial segmentation in LGE-MRI is an important purpose for diagnosis assistance of infarcted patients. Nevertheless, manual delineation of target volumes is time-consuming and depends on intra- and inter-observer variability. This thesis aims at developing efficient deep learning-based methods for automatically segmenting myocardial tissues (healthy myocardium, myocardial infarction, and microvascular obstruction) on LGE-MRI. In this regard, we first proposed a 2.5D SegU-Net model based on a fusion framework (U-Net and SegNet) to learn different feature representations adaptively. …
Extraction and fusion of spectral parameters for face recognition
2011
This is the copy of journal's version originally published in Proc. SPIE 7877: http://spie.org/x10.xml?WT.svl=tn7. Reprinted with permission of SPIE. Many methods have been developed in image processing for face recognition, especially in recent years with the increase of biometric technologies. However, most of these techniques are used on grayscale images acquired in the visible range of the electromagnetic spectrum. The aims of our study are to improve existing tools and to develop new methods for face recognition. The techniques used take advantage of the different spectral ranges, the visible, optical infrared and thermal infrared, by either combining them or analyzing them separately …
A Smoothed Particle Image Reconstruction method
2010
Many image processing techniques work with scattered data distribution usually employing grid based methods leading to numerical problems. To address this issue, a numerical method avoiding mesh generation can be used. Such a method performs an integral representation by means of a smoothing kernel function and, in the discrete formulation, involves domain particles. In this paper the meshless Smoothed Particle Hydrodynamics method is proposed in the Image Reconstruction context and a new computational strategy called Smoothed Particle Image Reconstruction is presented; the new method is based on a scatter approach and several innovative ideas are introduced in order to improve the computat…
Complexity of operations on cofinite languages
2010
International audience; We study the worst case complexity of regular operation on cofinite languages (i.e., languages whose complement is finite) and provide algorithms to compute efficiently the resulting minimal automata.
A new proposed cloud computing based architecture for space ground data systems
2018
International audience; Many companies decided to move towards the cloud computing technology in order to manage, monitor and explore huge space data amount in the space ground data systems. The corresponding transition adaptation needs a study to classify each space related service in the appropriate cloud computing service model layer; SaaS, PaaS and IaaS. In this paper, we specify a representation of proposed cloud computing based ground data system architecture for earth observation missions. The proposed architecture takes into consideration the collaboration aspect between multiple space agencies. We evaluate the time, storage and processing performance when using the cloud computing …
Inclusion of Instantaneous Influences in the Spectral Decomposition of Causality: Application to the Control Mechanisms of Heart Rate Variability
2021
Heart rate variability is the result of several physiological regulation mechanisms, including cardiovascular and cardiorespiratory interactions. Since instantaneous influences occurring within the same cardiac beat are commonplace in this regulation, their inclusion is mandatory to get a realistic model of physiological causal interactions. Here we exploit a recently proposed framework for the spectral decomposition of causal influences between autoregressive processes [2] and generalize it by introducing instantaneous couplings in the vector autoregressive model (VAR). We show the effectiveness of the proposed approach on a toy model, and on real data consisting of heart period (RR), syst…
A deep learning approach for the segmentation of myocardial diseases
2021
Cardiac left ventricular (LV) segmentation is a paramount essential step for both diagnosis and treatment of cardiac pathologies such as ischemia, myocardial infarction, arrhythmia and myocarditis. However, this segmentation is challenging due to high variability across patients and the potential lack of contrast between structures. In this work, we propose and evaluate a (2.5D) SegU-Net model based on the fusion of two deep learning segmentation techniques (U-Net and Seg-Net) for automated LGE-MRI (Late gadolinium enhanced magnetic resonance imaging) myocardial disease (infarct core and no-reflow region) quantification in a new multifield expert annotated dataset. Given that the scar tissu…