Search results for "oftware"

The Shape of Interoperability: Reviewing and Characterizing a Central Area within eGovernment Research

2012

Interoperability has been discussed and studied for more than a decade. While early discussions were very conceptual, interoperability is increasingly seen as one of the key enablers of the promised benefits from eGovernment. Consequently, and not surprisingly, a considerable amount of research has been published related to interoperability. However, a conceptual model of the interoperability domain is currently missing. We thus propose such a model based on an extensive review of a subset of the eGovernment literature published in highly ranked information systems, public administration and eGovernment journals. The model outlines and discusses actors, activities, contextual factors, effec…

Tool Support for Model Driven Development of Pervasive Systems

2007

This work presents the PervML Generative Tool (PervGT) that supports a model driven method for the development of pervasive services in ubiquitous environments. The tool, which is based on the Eclipse platform, provides facilities for the graphical description of pervasive systems using PervML, a UML-like modeling language. Once the pervasive system is specified, the PervML model is used as input to a transformation engine that generates source code and other implementation assets. This generated code extends an OSGi-based framework in order to build the final pervasive applications

The Brave New World of development in the internetwork computing architecture (InterNCA): or how distributed computing platforms will change systems …

1998

This essay is a speculation of the impact of the next generation technological platform — the internetwork computing architecture (InterNCA) — on systems development. The impact will be deep and pervasive and more substantial than when computing migrated from closed computer rooms to ubiquitous personal computers and flexible client-server solutions. Initially, by drawing upon the notion of a technological frame, the InterNCA, and how it differs from earlier technological frames, is examined. Thereafter, a number of hypotheses are postulated with regard to how the architecture will affect systems development content, scope, organization and processes. Finally, some suggestions for where the…

Support Vector Machines for Crop Classification Using Hyperspectral Data

2003

In this communication, we propose the use of Support Vector Machines (SVM) for crop classification using hyperspectral images. SVM are benchmarked to well–known neural networks such as multilayer perceptrons (MLP), Radial Basis Functions (RBF) and Co-Active Neural Fuzzy Inference Systems (CANFIS). Models are analyzed in terms of efficiency and robustness, which is tested according to their suitability to real–time working conditions whenever a preprocessing stage is not possible. This can be simulated by considering models with and without a preprocessing stage. Four scenarios (128, 6, 3 and 2 bands) are thus evaluated. Several conclusions are drawn: (1) SVM yield better outcomes than neura…

The indexing of persons in news sequences using audio-visual data

2004

We describe a video indexing system that automatically searches for a specific person in a news sequence. The proposed approach combines audio and video confidence values extracted from speaker and face recognition analysis. The system also incorporates a shot selection module that seeks for anchors, where the person on the scene is likely speaking. The system has been extensively tested on several news sequences with very good recognition rates.

Kernels for Remote Sensing Image Classification

2015

Classification of images acquired by airborne and satellite sensors is a very challenging problem. These remotely sensed images usually acquire information from the scene at different wavelengths or spectral channels. The main problems involved are related to the high dimensionality of the data to be classified and the very few existing labeled samples, the diverse noise sources involved in the acquisition process, the intrinsic nonlinearity and non-Gaussianity of the data distribution in feature spaces, and the high computational cost involved to process big data cubes in near real time. The framework of statistical learning in general, and of kernel methods in particular, has gained popul…

Detection of power line insulators on digital images with the use of laser spots

2019

The massive growth of technologies used to register and process digital images allow for their application in evaluating the technical condition of power lines. However, it is not possible without a set of dedicated methods for obtaining diagnostic information based on registered video data. The method described here details the detection of power line insulators in digital images featuring diversified backgrounds using laser spots. The algorithm of detecting an insulator in analysed images is based on testing the digital signal of pixel intensity profiles read between subsequent pairs of laser points in the image. The method is comprised of the following stages: import the image with laser…

A completely automated CAD system for mass detection in a large mammographic database

2006

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…

Multi-Temporal Image Classification with Kernels

2009

Finding essential features for tracking starfish in a video sequence

2004

The paper introduces a software system for detecting and tracking starfish in an underwater video sequence. The target of such a system is to help biologists in giving an estimate of the number of starfish present in a particular area of the sea-bottom. The nature of the input images is characterised by a low signal/noise ratio and by the presence of noisy background represented by pebbles; this makes the detection a non-trivial task. The procedure we use is a chain of several steps that starts from the extraction of the area of interest and ends with a classifier and a tracker providing the necessary information for counting the starfish present in the scene. © 2003 IEEE.