Search results for "Estimation"
showing 10 items of 924 documents
An efficient hardware implementation of Diamond Search motion estimation using CAL dataflow language
2011
Motion estimation represents a key module in video compression. The Reconfigurable Video Coding context (RVC) requires proposing a flexible solution for motion estimation. The motion estimation performance should be modified to fit with the user or the environment's constraints. Depending on the required performances fixed by the application, a full search is sometimes not suitable, hence, alternative fast/reduced solutions should be considered. In this paper, an efficient Diamond Search motion estimation, described in RVC-CAL actor language, is introduced. Starting from a high level description based CAL language, an automatic translation of the proposed CAL module to HDL is performed. Thi…
Machine Learning Methods for Spatial and Temporal Parameter Estimation
2020
Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…
Semisupervised kernel orthonormalized partial least squares
2012
This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…
Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis
2014
This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…
A family of kernel anomaly change detectors
2014
This paper introduces the nonlinear extension of the anomaly change detection algorithms in [1] based on the theory of reproducing kernels. The presented methods generalize their linear counterparts, under both the Gaussian and elliptically-contoured assumptions, and produce both improved detection accuracies and reduced false alarm rates. We study the Gaussianity of the data in Hilbert spaces with kernel dependence estimates, provide low-rank kernel versions to cope with the high computational cost of the methods, and give prescriptions about the selection of the kernel functions and their parameters. We illustrate the performance of the introduced kernel methods in both pervasive and anom…
Perceptually weighted optical flow for motion-based segmentation in MPEG-4 paradigm
2000
In the MPEG-4 paradigm, the sequence must be described in terms of meaningful objects. This meaningful, high-level representation should emerge from low-level primitives such as optical flow and prediction error which are the basic elements of previous-generation video coders. The accuracy of the high-level models strongly depends on the robustness of the primitives used. It is shown how perceptual weighting in optical flow computation gives rise to better motion estimates which consistently improve motion-based segmentation compared to equivalent unweighted motion estimates.
Design and Implementation of Acoustic Source Localization on a Low-Cost IoT Edge Platform
2020
The implementation of algorithms for acoustic source localization on edge platforms for the Internet of Things (IoT) is gaining momentum. Applications based on acoustic monitoring can greatly benefit from efficient implementations of such algorithms, enabling novel services for smart homes and buildings or ambient-assisted living. In this context, this brief proposes extreme low-cost sound source localization system composed of two microphones and the low power microcontroller module ESP32. A Direction-Of-Arrival (DOA) algorithm has been implemented taking into account the specific features of this board, showing excellent performance despite the memory constraints imposed by the platform. …
An algorithm for earthquakes clustering based on maximum likelihood
2007
In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…
Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel
2008
This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.
Cardiovascular risk assessment beyond Systemic Coronary Risk Estimation: A role for organ damage markers
2012
BACKGROUND: Cardiovascular risk assessment in the clinical practice is mostly based on risk charts, such as Framingham risk score and Systemic Coronary Risk Estimation (SCORE). These enable clinicians to estimate the impact of cardiovascular risk factors and assess individual cardiovascular risk profile. Risk charts, however, do not take into account subclinical organ damage, which exerts independent influence on risk and may amplify the estimated risk profile. Inclusion of organ damage markers in the assessment may thus contribute to improve this process. OBJECTIVE: Our aim was to evaluate the influence of implementation of SCORE charts with widely available indexes of organ damage, with t…