Search results for "image processing"

On the classification of dynamical data streams using novel “Anti-Bayesian” techniques

2018

Abstract The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, tha…

Lidar : traitement avancé des données et interprétation archéologique - cours 2016

2016

Client orientation in fashion e-commerce: a comparative study

2020

[EN] E-business and especially e-commerce represents one of the most important ways of doing business in the current context. That is why, researchers are doing a great effort in studying how Business to Consumer platforms manage the relationship with their clients. Regarding the most important channels of connection between consumers and companies in online market, the websites and Apps, this study analyzes the way in which e-businesses present the information to their clients from a client-focused strategy point of view. The present study evaluates how two important e-commerce companies deal with this in such a competitive market as fashion and aims to state some success guidelines for fa…

A multicriteria extension of the efficient market hypothesis

2021

Challenging the Efficient Market Hypothesis (EMH) has been a recurrent topic for researchers and practitioners since its formulation. Hundreds of empirical studies claim to either prove or disprove the EMH by means of a number of heterogeneous methods. Even though the EMH is usually adjusted to a measure of risk, there is a lack of a formal analysis within a multiple-criteria context. In this paper, we propose a extension of the EMH that accommodates the foundations of multiple-criteria decision analysis. To this end, we rely on a family of parametric signed dissimilarity measures to assess multidimensional performance differences. Since normalization is a critical step in our approach to a…

Dynamic scheduling of periodic skippable tasks in an overloaded real-time system

2008

International audience; The need for supporting dynamic real-time environments where changes in workloads may occur requires a scheduling framework that explicitly addresses overload conditions, allows the system to achieve graceful degradation and supports a mechanism capable of determining the load to be shed from the system to handle the overload. In applications ranging from video reception to air-craft control, tasks enter periodically and have response time constraints, but missing a deadline is acceptable, provided most deadlines are met. Such tasks are said to be occasionally skippable and have an assigned skip parameter. We look at the problem of uniprocessor scheduling of skippabl…

Latent force models for earth observation time series prediction

2016

We introduce latent force models for Earth observation time series analysis. The model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. The LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions.

Data service platform for sentinel-2 surface reflectance and value-added products: System use and examples

2016

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.…

Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval

2017

Remote sensing data analysis is knowing an unprecedented upswing fostered by the activities of the public and private sectors of geospatial and environmental data analysis. Modern imaging sensors offer the necessary spatial and spectral information to tackle a wide range problems through Earth Observation, such as land cover and use updating, urban dynamics, or vegetation and crop monitoring. In the upcoming years even richer information will be available: more sophisticated hyperspectral sensors with high spectral resolution, multispectral sensors with sub-metric spatial detail or drones that can be deployed in very short time lapses. Besides such opportunities, these new and wealthy infor…

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

2021

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …

Combinatorial Double Auction Radio Resource Allocation Model in Crowd Networks

2018

International audience; Industrial Partners (IPs) with Mobile Network Operators (MNOs) are extending the mobile network infrastructure with Small Cells (SCs) in order to meet the growing mobile traffic demand. Due to the increasing number of telecommunication market competitors and the scarcity of radio resources, static sharing schemes are no more efficient. New dynamic schemes should be considered to meet both user expectations and economic success. In a crowd networking context, we propose in this work a dynamic radio resource scheme based on combinatorial double auctions. The participants in these auctions are the MNOs considered as buyers and the IPs, providers of SCs, considered as se…