Flame Detection for Video-based Early Fire Warning Systems and 3D Visualization of Fire Propagation

Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.The original article was published by ACTAPRESS and is available here: http://www.actapress.com/Content_of_Proceeding.aspx?proceedingid=73

Video Based Flame Detection Using Spatio-Temporal Features and SVM Classification

Video-based surveillance systems can be used for early fire detection and localization in order to minimize the damage and casualties caused by wildfires. However, reliability of these systems is an important issue and therefore early detection versus false alarm rate has to be considered. In this paper, we present a new algorithm for video based flame detection, which identifies spatio-temporal features of fire such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. For each candidate region of an image a feature vector is generated and used as input to an SVM classifier, which discriminates between fire and fire-coloured regions. Experimental results show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio

Fire detection, fuel model estimation and fire propagation estimation/visualization for the protection of Cultural Heritage

FIRESENSE (Fire Detection and Management through a Multi-Sensor Network for the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme Weather Conditions) is a project co-funded by EU FP7 Environment that aims to develop a multi-sensor early warning system to remotely monitor areas of archaeological and cultural interest from the risk of fire and extreme weather conditions. It will combine different sensing technologies, i.e. wireless networks of temperature/humidity sensors, optical and infrared cameras, as well as local weather stations. Pilot deployments will be made in five cultural heritage sites in Greece, Turkey, Italy and Tunisia. Another goal is the estimation of the propagation direction and speed in order to help forest fire management. FIRESENSE will provide real-time information about the evolution of fire using wireless sensor network data and estimate the propagation of the fire based on the fuel model of the area and other important parameters such as wind speed, slope, and aspect of the ground surface. The fire propagation data are visualized on a user-friendly 3D-GIS environment. Some of the supported features are: a) Display of sensor locations and regions of interest in the cultural sites b) Interactive selection of some parameters (e.g. ignition point, humidity parameters) c) Automatic acquisition of weather data from onsite or nearby weather stations d) 2-D or 3-D visualization of fire propagation estimation output (ignition time and flame length). Commercial satellite images have reached a fairly high spatial resolution which allows more powerful textural analyses and more detailed description of soil surface. This improves the capacity to recognize and classify land uses, the amount and typology of vegetation and other potential sources of fuel for wildfires. It also reduced substantially the time and costs for updating vegetation and fuel distribution. Ground truth is also required especially for developing and testing of new image analysis algorithms. Measurements of the main fuel component are required and are usually destructive and costly, sometimes even unacceptable, especially if biodiversity or soil are threatened or in protected sites. Therefore, a sampling technique has been developed for single or groups of plants. Sub-volumes, which are characterized by the same type of fuel component and vegetation mix, are sampled over small known volumes. Volumetric mass densities are transformed into biomass and fuel components as mass per unit of surface. Very-High-resolution satellite images (QuickBird) are ortho-rectified with a detailed DTM of the study area and analyzed: recognition of lines of water flux convergence, pathways, usually unrecorded on official maps, vegetation patchiness, connectivity lines for fire to spread more easily, and connectivity lines for water fluxes during rainstorms will be among the results. Another approach that we use for vegetation classification is multi-band SVM classification approach. Each band characterizes/emphasizes a particular type of information such as textural, spatial, local and spectral information. The combination of these features improves significantly the accuracy of the results. We are currently investigating the registration between the ortho-rectified images and a ground truth map from the covered area in order to validate and improve the classification results. It is expected that the characterization of these areas and the accumulation of temporal series of vegetation/fuel distribution will serve not just for fire prevention and management but also for soil conservation and soil erosion control

Multi-manifold Attention for Vision Transformers

Vision Transformers are very popular nowadays due to their state-of-the-art performance in several computer vision tasks, such as image classification and action recognition. Although their performance has been greatly enhanced through highly descriptive patch embeddings and hierarchical structures, there is still limited research on utilizing additional data representations so as to refine the selfattention map of a Transformer. To address this problem, a novel attention mechanism, called multi-manifold multihead attention, is proposed in this work to substitute the vanilla self-attention of a Transformer. The proposed mechanism models the input space in three distinct manifolds, namely Euclidean, Symmetric Positive Definite and Grassmann, thus leveraging different statistical and geometrical properties of the input for the computation of a highly descriptive attention map. In this way, the proposed attention mechanism can guide a Vision Transformer to become more attentive towards important appearance, color and texture features of an image, leading to improved classification and segmentation results, as shown by the experimental results on well-known datasets.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Multimodal Affective State Recognition in Serious Games Applications

A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify the affective state of players during gameplay scenarios. More specifically, two monomodal classifiers have been designed for extracting affective state information based on facial expression and body motion analysis. For the combination of different modalities a deep model is proposed that is able to make a decision about player’s affective state, while also being robust in the absence of one information cue. In order to evaluate the performance of our methodology, a bimodal database was created using Microsoft’s Kinect sensor, containing feature vectors extracted from users' facial expressions and body gestures. The proposed method achieved higher recognition rate in comparison with mono-modal, as well as early-fusion algorithms. Our methodology outperforms all other classifiers, achieving an overall recognition rate of 98.3%

Motivational Principles and Personalisation Needs for Geo-Crowdsourced Intangible Cultural Heritage Mobile Applications

Whether it’s for altruistic reasons, personal gains, or third party’s interests, users are influenced by different kinds of motivations when making use of mobile geo-crowdsourcing applications (geoCAs). These reasons, extrinsic and/or intrinsic, must be factored in when evaluating the use intention of these applications and how effective they are. A functional geoCA, particularly if designed for Volunteered Geographic Information (VGI), is the one that persuades and engages its users, by accounting for their diversity of needs across a period of time. This paper explores a number of proven and novel motivational factors destined for the preservation and collection of Intangible Cultural Heritage (ICH) through geoCAs. By providing an overview of personalisation research and digital behaviour interventions for geo-crowdsoured ICH, the paper examines the most relevant usability and trigger factors for different crowd users, supported by a range of technology-based principles. In addition, we present the case of StoryBee, a mobile geoCA designed for “crafting stories” by collecting and sharing users’ generated content based on their location and favourite places. We conclude with an open-ended discussion about the ongoing challenges and opportunities arising from the deployment of geoCAs for ICH

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computer-based classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases – particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns

Virtual reality and deformable modeling in web-based medical education

Οι εφαρμογές προσομοιώσεων μέσω του διαδικτύου με σκοπό την εξ αποστάσεως εκπαίδευση στην ιατρική αποτελούν μία ανερχόμενη και πολλά υποσχόμενη τεχνολογία, η οποία αναμένεται τα επόμενα χρόνια να διαδραματίσει σημαντικό ρόλο στην εκπαίδευση των φοιτητών της ιατρικής. Πέρα από τα σημαντικά πλεονεκτήματα που επιτυγχάνονται από τη χρήση του διαδικτύου ως μέσου μετάδοσης της πληροφορίας, ο απώτερος σκοπός της νέας αυτής τεχνολογίας είναι να αποτελέσει το έναυσμα της δημιουργίας μιας διαδικτυακής πλατφόρμας, η οποία θα είναι προσβάσιμη από όλους τους χρήστες του διαδικτύου και θα αποτελείται από ένα πλήθος προσομοιώσεων για την κάλυψη ενός μεγάλου φάσματος ιατρικών εφαρμογών στα πλαίσια μιας ενοποιημένης διαδικτυακής εκπαίδευσης. Η ανάπτυξη, ωστόσο, τέτοιου είδους εφαρμογών παραμένει ακόμα περιορισμένη παρά τις σύγχρονες δυνατότητες που προσφέρουν τόσο η τεχνολογία του διαδικτύου όσο και αυτή της εικονικής πραγματικότητας. Ο λόγος είναι οι υψηλές απαιτήσεις των εφαρμογών αυτών από πλευράς ρεαλισμού και ταχύτητας καθιστώντας απαραίτητη τη ρεαλιστική μοντελοποίηση τόσο της αναπαράστασης όσο και της συμπεριφοράς των τρισδιάστατων μοντέλων. Ιδιαίτερα στην περίπτωση της μοντελοποίησης της συμπεριφοράς των ιατρικών μοντέλων απαιτείται η χρησιμοποίηση κατάλληλων αλγορίθμων, οι οποίοι ονομάζονται μοντέλα παραμόρφωσης, που θα επιτυγχάνουν ρεαλιστικά αποτελέσματα σε πραγματικό χρόνο. Στα πλαίσια της παρούσας διδακτορικής διατριβής επιχειρήθηκε η μελέτη του παραπάνω ερευνητικού χώρου με σκοπό την ανάπτυξη μιας νέας μεθόδου μοντελοποίησης της παραμόρφωσης λαμβάνοντας υπόψη τις συγκεκριμένες απαιτήσεις των ιατρικών διαδικτυακών προσομοιώσεων. Παράλληλα, επιχειρήθηκε η ανάπτυξη μιας εφαρμογής διαδικτυακής ιατρικής προσομοίωσης, σε συνεργασία με την Ιατρική Σχολή του Αριστοτελείου Πανεπιστημίου, αποσκοπώντας στην τρισδιάστατη αναπαράσταση της κυτταρικής δομής του ανθρώπινου ήπατος αλλά και στην παρουσίαση της εξέλιξης μιας συγκεκριμένης παθολογικής κατάστασης με τη βοήθεια του μοντέλου παραμόρφωσης, που αναπτύχθηκε στα πλαίσια της παρούσας διδακτορικής διατριβής. Τέλος, μελετώντας το γενικότερο πρόβλημα της εξ αποστάσεως εκπαίδευσης των φοιτητών της ιατρικής και θεωρώντας ότι η ιατρική εκπαίδευση δε μπορεί να περιορίζεται απλώς στην ανάπτυξη συστημάτων προσομοιώσεων, επιχειρήθηκε η καινοτόμος σχεδίαση και ανάπτυξη ενός ολοκληρωμένου υπερμεσικού εκπαιδευτικού εικονικού περιβάλλοντος, το οποίο συνδυάζει χαρακτηριστικά που αναπτύσσονται σε μία πραγματική εκπαιδευτική διαδικασία με τη δυνατότητα χρήσης ιατρικών προσομοιώσεων

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems