A Framework for Psychophysiological Classification within a Cultural Heritage Context Using Interest

This article presents a psychophysiological construct of interest as a knowledge emotion and illustrates the importance of interest detection in a cultural heritage context. The objective of this work is to measure and classify psychophysiological reactivity in response to cultural heritage material presented as visual and audio. We present a data processing and classification framework for the classification of interest. Two studies are reported, adopting a subject-dependent approach to classify psychophysiological signals using mobile physiological sensors and the support vector machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material using psychophysiological feature data and a machine learning approach, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience designed to adapt the provision of information to sustain the interest of the visitor

Oil Spill Detection using Segmentation based Approaches

This paper presents a description and comparison of two segmentation methods for the oil spill detection in the sea surface. SLAR sensors acquire video sequences from which snapshots are extracted for the detection of oil spills. Both approaches are segmentation based on graph techniques and J-image respectively. Finally, the aim of applying both approaches to SLAR snapshots, as shown, is to detect the largest part of the oil slick and minimize the false detection of the spill.This work was funded by Ministry of Economy and Competitiveness and supported by Spanish project (RTC-2014-1863-8)

Técnicas de segmentación en imágenes SLAR para la detección de vertidos de hidrocarburos

[Resumen] En este artículo se presentan dos métodos de segmentación para la detección de vertidos de hidrocarburos en la superficie marítima a partir de imágenes obtenidas por un sensor SLAR embarcado en una aeronave. Para ello, se describen y comparan dos aproximaciones de segmentación, basadas en grafo e imagen-J, respectivamente. Finalmente, se muestra el resultado de aplicar ambas aproximaciones a imágenes SLAR, buscando como objetivo detectar la mayor área de vertido en la superficie marina al tiempo que se minimiza la falsa detección de ésta.Este trabajo ha sido financiado por el proyecto (RTC-2014-1863-8) “ONTIME: Operación remota de Transmisión de Información en Misiones de Emergencia” de la Convocatoria Retos de Colaboración del MINECOhttps://doi.org/10.17979/spudc.978849749808

Analysis of Polarimetric Synthetic Aperture Radar and Passive Visible Light Polarimetric Imaging Data Fusion for Remote Sensing Applications

The recent launch of spaceborne (TerraSAR-X, RADARSAT-2, ALOS-PALSAR, RISAT) and airborne (SIRC, AIRSAR, UAVSAR, PISAR) polarimetric radar sensors, with capability of imaging through day and night in almost all weather conditions, has made polarimetric synthetic aperture radar (PolSAR) image interpretation and analysis an active area of research. PolSAR image classification is sensitive to object orientation and scattering properties. In recent years, significant work has been done in many areas including agriculture, forestry, oceanography, geology, terrain analysis. Visible light passive polarimetric imaging has also emerged as a powerful tool in remote sensing for enhanced information extraction. The intensity image provides information on materials in the scene while polarization measurements capture surface features, roughness, and shading, often uncorrelated with the intensity image. Advantages of visible light polarimetric imaging include high dynamic range of polarimetric signatures and being comparatively straightforward to build and calibrate. This research is about characterization and analysis of the basic scattering mechanisms for information fusion between PolSAR and passive visible light polarimetric imaging. Relationships between these two modes of imaging are established using laboratory measurements and image simulations using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. A novel low cost laboratory based S-band (2.4GHz) PolSAR instrument is developed that is capable of capturing 4 channel fully polarimetric SAR image data. Simple radar targets are formed and system calibration is performed in terms of radar cross-section. Experimental measurements are done using combination of the PolSAR instrument with visible light polarimetric imager for scenes capturing basic scattering mechanisms for phenomenology studies. The three major scattering mechanisms studied in this research include single, double and multiple bounce. Single bounce occurs from flat surfaces like lakes, rivers, bare soil, and oceans. Double bounce can be observed from two adjacent surfaces where one horizontal flat surface is near a vertical surface such as buildings and other vertical structures. Randomly oriented scatters in homogeneous media produce a multiple bounce scattering effect which occurs in forest canopies and vegetated areas. Relationships between Pauli color components from PolSAR and Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging are established using real measurements. Results show higher values of the red channel in Pauli color image (|HH-VV|) correspond to high DOLP from double bounce effect. A novel information fusion technique is applied to combine information from the two modes. In this research, it is demonstrated that the Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging can be used for separation of the classes in terms of scattering mechanisms from the PolSAR data. The separation of these three classes in terms of the scattering mechanisms has its application in the area of land cover classification and anomaly detection. The fusion of information from these particular two modes of imaging, i.e. PolSAR and passive visible light polarimetric imaging, is a largely unexplored area in remote sensing and the main challenge in this research is to identify areas and scenarios where information fusion between the two modes is advantageous for separation of the classes in terms of scattering mechanisms relative to separation achieved with only PolSAR

Οπτική ανίχνευση δασικών πυρκαγιών σε πραγματικό χρόνο με χρήση ψηφιακού επεξεργαστή

Σε μια χώρα όπως η Ελλάδα, στην οποία το φαινόμενο των δασικών πυρκαγιών είναι συνηθισμένο τους καλοκαιρινούς μήνες, η εξέλιξη της πληροφορικής μπορεί να συμβάλει σε μεγάλο βαθμό στην πρόληψη και στην έγκαιρη ανίχνευση πυρκαγιών. Η υλοποίηση αυτόνομων έξυπνων συστημάτων τα οποία μπορούν σε πραγματικό χρόνο να ανιχνεύουν την ύπαρξη πυρκαγιάς, θα μπορούσε να είναι ένα σημαντικό εργαλείο για την διάσωση των Ελληνικών δασών. Στα πλαίσια αυτής εργασίας, θα περιγραφεί ένα σύστημα ψηφιακής επεξεργασίας σήματος, το οποίο λαμβάνει δεδομένα από μια διαδικτυακή κάμερα, τα επεξεργάζεται και αποφασίζει αν υπάρχει ένδειξη πυρκαγιάς. Το σημαντικό πλεονέκτημα αυτού του συστήματος είναι ότι είναι αυτόνομο, δηλαδή δεν χρειάζεται τα δεδομένα να σταλθούν σε ένα κεντρικό υπολογιστικό σύστημα, το οποίο θα ήταν ιδιαίτερα χρονοβόρο, και με την χρήση μια πηγής ενέργειας, για παράδειγμα ένα ηλιακό πάνελ, θα μπορούσε να ανιχνεύει ένα χώρο και να ειδοποιεί τον διαχειριστή για την ύπαρξη φωτιάς. Τα αποτελέσματα αξιολόγησης είναι ιδιαίτερα ενθαρρυντικά. Όμως η οπτική ανάλυση ενός χαοτικού φαινομένου όπως πυρκαγιά, δεν είναι ποτέ αρκετή. Ο συνδυασμός της ανάλυσης αυτής με χρήση διαφορών αισθητήρων όπως υγρασίας θερμοκρασίας, μπορεί να οδηγήσει σε ασφαλή συμπεράσματα. Κάτι τέτοιο όμως ξεφεύγει από τα πλαίσια της παρούσας εργασίας.In a country as Greece, in which the phenomenon of forestal fires is usual especially in the summer months, the improvement of information technology can contribute to a great degree in the prevention and in the convenient detection of fires. The implementation of autonomous intelligent systems which can in real time detect the existence of fire, it could be an important tool for the rescue of Greek forests. In this project, will be described a digital signal processing system, which receives data from a network camera, processes that data and decides if exists clue of fire. The important advantage of this system is that is autonomous, and does not need sent the data in a central computing system which would be particularly time consuming, and using an energy source, for example a solar panel, could detect a space and notify the administrator for the existence of fire. The results of evaluation are particularly encouraging. However the optical analysis of chaotic phenomenon as fire is not enough. The combination of this analysis in combination of using humidity and temperature sensors, can lead to sure conclusions. But this analysis is out of this master thesis content

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Exploring the Biocybernetic loop: Classifying Psychophysiological Responses to Cultural Artefacts using Physiological Computing

The aim of this research project was to provide a bio-sensing component for a real-time adaptive technology in the context of cultural heritage. The proposed system was designed to infer the interest or intention of the user and to augment elements of the cultural heritage experience interactively through implicit interaction. Implicit interaction in this context is the process whereby the system observes the user while they interact with artefacts; recording psychophysiological responses to cultural heritage artefacts or materials and acting upon these responses to drive adaptations in content in real-time.Real-time biocybernetic control is the central component of physiological computing wherein physiological data are converted into a control input for a technological system. At its core the bio-sensing component is a biocybernetic control loop that utilises an inference of user interest as its primary driver. A biocybernetic loop is composed of four main stages: inference, classification, adaptation and interaction. The programme of research described in this thesis is concerned primarily with exploration of the inference and classification elements of the biocybernetic loop but also encompasses an element of adaptation and interaction. These elements are explored first through literature review and discussion (presented in chapters 1-5) and then through experimental studies (presented in chapters 7-11)