Search results for "video processing"
showing 10 items of 56 documents
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
2019
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegeta…
Capturing and Indexing Rehearsals: The Design and Usage of a Digital Archive of Performing Arts
2015
International audience; Preserving the cultural heritage of the performing arts raises difficult and sensitive issues, as each performance is unique by nature and the juxtaposition between the performers and the audience cannot be easily recorded. In this paper, we report on an experimental research project to preserve another aspect of the performing arts—the history of their rehearsals. We have specifically designed non-intrusive video recording and on-site documentation techniques to make this process transparent to the creative crew, and have developed a complete workflow to publish the recorded video data and their corresponding meta-data online as Open Data using state-of-the-art audi…
Portable Video Supercomputing
2004
As inexpensive imaging chips and wireless telecommunications are incorporated into an increasing array, of portable products, the need for high efficiency, high throughput embedded processing will become an important challenge in computer architecture. Videocentric applications, such wireless videoconferencing, real-time video enhancement and analysis, and new, immersive modes of distance education, will exceed the computational capabilities of current microprocessor and digital signal processor (DSP) architectures. A new class of embedded computers, portable video supercomputers, will combine supercomputer performance with the energy efficiency required for deployment in portable systems. …
A real-time non-intrusive FPGA-based drowsiness detection system
2011
Automotive has gained several benefits from the Ambient Intelligent researches involving the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs. One of the main topics in automotive is to anticipate driver needs and safety, in terms of preventing critical and dangerous events. Considering the high number of caused accidents, one of the most relevant dangerous events affecting driver and passengers safety is driver’s drowsiness and hypovigilance. This paper presents a low-intrusive, real-time driver’s drowsiness detection system for common vehicles. The proposed system exploits the ‘‘bright p…
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
2020
Abstract With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, w…
Flood Detection On Low Cost Orbital Hardware
2019
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability to automatically identify flooded regions in images. We describe a prototypical flood segmentation system, identifying cloud, water and land, that could be deployed on a constellation of small satellites, performing processing on board to reduce downlink bandwidth by 2 orders of magnitude. We target PhiSat-1, part of the FSSCAT mission, which is planned to be launched by the …
Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection
2021
The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two…
Learning User's Confidence for Active Learning
2013
In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene. We propose a filtering scheme based on a classifier that learns the confidence of the user in labeling, thus minimizing the queries where the user would not be able to provide a class for the pixel. The capacity of a model to learn the user's confidence is studied in detail, also showing the effect of resolution is such a …
Efficient Nonlinear RX Anomaly Detectors
2020
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks t…
Local-Area-Learning Network: Meaningful Local Areas for Efficient Point Cloud Analysis
2020
Research in point cloud analysis with deep neural networks has made rapid progress in recent years. The pioneering work PointNet offered a direct analysis of point clouds. However, due to its architecture PointNet is not able to capture local structures. To overcome this drawback, the same authors have developed PointNet++ by applying PointNet to local areas. The local areas are defined by center points and their neighbors. In PointNet++ and its further developments the center points are determined with a Farthest Point Sampling (FPS) algorithm. This has the disadvantage that the center points in general do not have meaningful local areas. In this paper, we introduce the neural Local-Area-L…