<p>This thesis explores the concepts of crowdsourcing and the ability of diversity, applied to optical mine detection. The idea is to use the human eye and wide and diverse workforce available on the Internet to detect mines, in addition to computer algorithms.</p><p>The theory of diversity in problem solving is discussed, especially the Diversity Trumps Ability Theorem and the Diversity Prediction Theorem, and how they should be carried out for possible applications such as contrast interpretation and area reduction respectively.</p><p>A simple contrast interpretation experiment is carried out comparing the results of a laymen crowd and one of experts, having the crowds examine extracts from hyperspectral images, classifying the amount of objects or mines and the type of terrain. Due to poor participation rate of the expert group, and an erroneous experiment introduction, the experiment does not yield any statistically significant results. Therefore, no conclusion is made.</p><p>Experiment improvements are proposed as well as possible future applications.</p> / <p>Denna rapport går igenom tanken bakom <em>crowdsourcing</em> och mångfaldens styrka tillämpad på optisk mindetektering. Tanken är att använda det mänskliga ögat och Internets skiftande och varierande arbetsstyrka som ett tillägg för att upptäcka minor tillsammans med dataalgoritmer.</p><p>Mångfaldsteorin i problemlösande diskuteras och speciellt ''Diversity Trumps Ability''-satsen och ''Diversity Prediction''-satsen och hur de ska genomföras för tillämpningar som kontrastigenkänning respektive ytreduktion.</p><p>Ett enkelt kontrastigenkänningsexperiment har genomförts för att jämföra resultaten mellan en lekmannagrupp och en expertgrupp. Grupperna tittar på delar av data från hyperspektrala bilder och klassifierar andel objekt eller minor och terrängtyp. På grund av lågt deltagande från expertgruppen och en felaktig experimentintroduktion ger inte experimentet några statistiskt signifikanta resultat, varför ingen slutsats dras.</p><p>Experimentförbättringar och framtida tillämpningar föreslås.</p> / Multi Optical Mine Detection System

<p>This master's thesis explores whether time-frequency techniques can be utilized in a ground penetrating radar system. The system studied is the HUMUS system which has been developed at FOI, and which is used for the detection and classification of buried land mines. </p><p>The objective of this master's thesis is twofold. First of all it is supposed to give a theoretical introduction to the wavelet transform and wavelet packets, and also to introduce general time-frequency transformations. Secondly, the thesis presents and implements an adaptive method, which is used to perform the task of a feature extractor. </p><p>The wavelet theory presented in this thesis gives a first introduction to the concept of time-frequency transformations. The wavelet transform and wavelet packets are studied in detail. The most important goal of this introduction is to define the theoretical background needed for the second objective of the thesis. However, some additional concepts will also be introduced, since they were deemed necessary to include in an introduction to wavelets. </p><p>To illustrate the possibilities of wavelet techniques in the existing HUMUS system, one specific application has been chosen. The application chosen is feature extraction. The method for feature extraction described in this thesis uses wavelet packets to transform theoriginal radar signal into a form where the features of the signal are better revealed. One of the algorithms strengths is its ability to adapt itself to the kind of input radar signals expected. The algorithm will pick the "best" wavelet packet from a large number of possible wavelet packets.</p><p>The method we use in this thesis emanates from a previously publicized dissertation. The method proposed in that dissertation has been modified to the specific environment of the HUMUS system. It has also been implemented in MATLAB, and tested using data obtained by the HUMUS system. The results are promising; even"weak"objects can be revealed using the method.</p>

This thesis explores the concepts of crowdsourcing and the ability of diversity, applied to optical mine detection. The idea is to use the human eye and wide and diverse workforce available on the Internet to detect mines, in addition to computer algorithms. The theory of diversity in problem solving is discussed, especially the Diversity Trumps Ability Theorem and the Diversity Prediction Theorem, and how they should be carried out for possible applications such as contrast interpretation and area reduction respectively. A simple contrast interpretation experiment is carried out comparing the results of a laymen crowd and one of experts, having the crowds examine extracts from hyperspectral images, classifying the amount of objects or mines and the type of terrain. Due to poor participation rate of the expert group, and an erroneous experiment introduction, the experiment does not yield any statistically significant results. Therefore, no conclusion is made. Experiment improvements are proposed as well as possible future applications. / Denna rapport går igenom tanken bakom crowdsourcing och mångfaldens styrka tillämpad på optisk mindetektering. Tanken är att använda det mänskliga ögat och Internets skiftande och varierande arbetsstyrka som ett tillägg för att upptäcka minor tillsammans med dataalgoritmer. Mångfaldsteorin i problemlösande diskuteras och speciellt ''Diversity Trumps Ability''-satsen och ''Diversity Prediction''-satsen och hur de ska genomföras för tillämpningar som kontrastigenkänning respektive ytreduktion. Ett enkelt kontrastigenkänningsexperiment har genomförts för att jämföra resultaten mellan en lekmannagrupp och en expertgrupp. Grupperna tittar på delar av data från hyperspektrala bilder och klassifierar andel objekt eller minor och terrängtyp. På grund av lågt deltagande från expertgruppen och en felaktig experimentintroduktion ger inte experimentet några statistiskt signifikanta resultat, varför ingen slutsats dras. Experimentförbättringar och framtida tillämpningar föreslås. / Multi Optical Mine Detection System

This master's thesis explores whether time-frequency techniques can be utilized in a ground penetrating radar system. The system studied is the HUMUS system which has been developed at FOI, and which is used for the detection and classification of buried land mines. The objective of this master's thesis is twofold. First of all it is supposed to give a theoretical introduction to the wavelet transform and wavelet packets, and also to introduce general time-frequency transformations. Secondly, the thesis presents and implements an adaptive method, which is used to perform the task of a feature extractor. The wavelet theory presented in this thesis gives a first introduction to the concept of time-frequency transformations. The wavelet transform and wavelet packets are studied in detail. The most important goal of this introduction is to define the theoretical background needed for the second objective of the thesis. However, some additional concepts will also be introduced, since they were deemed necessary to include in an introduction to wavelets. To illustrate the possibilities of wavelet techniques in the existing HUMUS system, one specific application has been chosen. The application chosen is feature extraction. The method for feature extraction described in this thesis uses wavelet packets to transform theoriginal radar signal into a form where the features of the signal are better revealed. One of the algorithms strengths is its ability to adapt itself to the kind of input radar signals expected. The algorithm will pick the "best" wavelet packet from a large number of possible wavelet packets. The method we use in this thesis emanates from a previously publicized dissertation. The method proposed in that dissertation has been modified to the specific environment of the HUMUS system. It has also been implemented in MATLAB, and tested using data obtained by the HUMUS system. The results are promising; even"weak"objects can be revealed using the method.

No description available.

<p>Landminor är ett stort problem både under och efter krigstid. De metoder som används för att detektera minor har inte ändrats mycket sedan 1940-talet. Forskning med mål att utvärdera olika elektro-optiska sensorer och metoder som skulle kunna användas för att skapa mer effektiv min-detektion genomförs på FOI. Försök som har gjorts med data från bland annat laser-radar och IR-sensorer har gett intressanta resultat.</p><p>I det här examensarbetet utvärderades olika fenomen och egenskaper i laser-radar- och IR-data. De testade egenskaperna var intensitet, IR, ytlikhet och höjd.</p><p>En metod som segmenterar intressanta objekt och bakgrundsdata utformades och implementerades. Metoden använde sig av expectation-maximization-skattning och ett minimum message length-kriterium. Ett scatter separability-kriterium användes för att bestämma kvalitén på de olika egenskaperna och på den resulterande segmenteringen.</p><p>Data insamlad under en mätkampanj av FOI användes för att testa metoden. Resultatet visade bland annat att ytlikhetsmåttet gav en bra segmentering för stora objekt med släta ytor, men var sämre för små objekt med skrovliga ytor. Vid jämförelse med en manuellt skapad mål-mask visade det sig att metoden klarade av att välja ut egenskaper som i många fall gav en godkänd segmentering.</p> / <p>Land mines are a huge problem in conflict time and after. Methods used to detect mines have not changed much since the 1940's. Research aiming to evaluate output from different electro-optical sensors and develop methods for more efficient mine detection is performed at FOI. Early experiments with laser radar sensors show promising results, as do analysis of data from infrared sensors.</p><p>In this thesis, an evaluation is made of features found in laser radar- and in infrared -sensor data. The tested features are intensity, infrared, a surfaceness feature extracted from the laser radar data and height above an estimated ground plane.</p><p>A method for segmenting interesting objects from background data using theexpectation-maximization algorithm and a minimum message length criterion is designed and implemented. A scatter separability criterion is utilized to determine the quality of the features and the resulting segmentation.</p><p>The method is tested on real data from a field trial performed by FOI. The results show that the surfaceness feature supports the segmentation of larger object with smooth surfaces but gives no contribution to small object with irregular surfaces. The method produces a decent result of selecting contributing features for different neighbourhoods of a scene. A comparison with a manually created target mask of the neighbourhood and the segmented components show that in most cases a high percentage separation of mine data and background data is possible.</p>

Landminor är ett stort problem både under och efter krigstid. De metoder som används för att detektera minor har inte ändrats mycket sedan 1940-talet. Forskning med mål att utvärdera olika elektro-optiska sensorer och metoder som skulle kunna användas för att skapa mer effektiv min-detektion genomförs på FOI. Försök som har gjorts med data från bland annat laser-radar och IR-sensorer har gett intressanta resultat. I det här examensarbetet utvärderades olika fenomen och egenskaper i laser-radar- och IR-data. De testade egenskaperna var intensitet, IR, ytlikhet och höjd. En metod som segmenterar intressanta objekt och bakgrundsdata utformades och implementerades. Metoden använde sig av expectation-maximization-skattning och ett minimum message length-kriterium. Ett scatter separability-kriterium användes för att bestämma kvalitén på de olika egenskaperna och på den resulterande segmenteringen. Data insamlad under en mätkampanj av FOI användes för att testa metoden. Resultatet visade bland annat att ytlikhetsmåttet gav en bra segmentering för stora objekt med släta ytor, men var sämre för små objekt med skrovliga ytor. Vid jämförelse med en manuellt skapad mål-mask visade det sig att metoden klarade av att välja ut egenskaper som i många fall gav en godkänd segmentering. / Land mines are a huge problem in conflict time and after. Methods used to detect mines have not changed much since the 1940's. Research aiming to evaluate output from different electro-optical sensors and develop methods for more efficient mine detection is performed at FOI. Early experiments with laser radar sensors show promising results, as do analysis of data from infrared sensors. In this thesis, an evaluation is made of features found in laser radar- and in infrared -sensor data. The tested features are intensity, infrared, a surfaceness feature extracted from the laser radar data and height above an estimated ground plane. A method for segmenting interesting objects from background data using theexpectation-maximization algorithm and a minimum message length criterion is designed and implemented. A scatter separability criterion is utilized to determine the quality of the features and the resulting segmentation. The method is tested on real data from a field trial performed by FOI. The results show that the surfaceness feature supports the segmentation of larger object with smooth surfaces but gives no contribution to small object with irregular surfaces. The method produces a decent result of selecting contributing features for different neighbourhoods of a scene. A comparison with a manually created target mask of the neighbourhood and the segmented components show that in most cases a high percentage separation of mine data and background data is possible.

Les mines sous marines sont très utilisées dans les conflits. Pour contrer cette menace, les marines s'équipent de moyens de lutte anti-mine autonomes afin d'éviter l'intervention humaine. Une mission de guerre des mines se découpe en quatre étapes distinctes : la détection des objets, la classification et l'identification puis la neutralisation. Cette thèse propose des solutions algorithmiques pour l'étape d'identification par caméra vidéo. Le drone d'identification connaît la position approximative de l'objet à identifier. La première mission de ce drone est de re-détecter l'objet avant de le classifier et de l'identifier. Le milieu sous-marin perturbe les images acquises par la caméra (absorption, diffusion). Pour faciliter la détection et la reconnaissance (classification et identification), nous avons prétraité les images. Nous avons proposé deux méthodes de détection des objets. Tout d'abord nous modifions le spectre de l'image afin d'obtenir une image dans laquelle il est possible de détecter les contours des objets. Une seconde méthode a été développée à partir de la soustraction du fond, appris en début de séquence vidéo. Les résultats obtenus avec cette seconde méthode ont été comparés à une méthode existante. Lorsqu'il y a une détection, nous cherchons à reconnaître l'objet. Pour cela, nous utilisons la corrélation. Les images de référence ont été obtenues à partir d'images de synthèse 3D des mines. Pour les différentes méthodes utilisées, nous avons optimisés les résultats en utilisant les informations de navigation. En effet, selon les déplacements du drone, nous pouvons fixer des contraintes qui vont améliorer la détection et réduire le temps de calcul nécessaire à l'identification. / To avoid the underwater mine threat and to limit human interventions, navies use autonomous underwater vehicles. An underwater mine warfare mission is divided into four steps : object detection, classification, identification and neutralization. This PhD thesis proposes algorithmic solutions for the identification step done with a video camera. Thanks to the detection step, the identification vehicle knows approximately the object position. First, the vehicle has to detect and position this object exactly. Then it will be classified and identified. The underwater medium affects the images acquired with a video camera through absorption and scattering. The first step of our algorithm is to preprocess the images to help the detection and recognition (classification and identification) steps.We have proposed two detection methods. The first one consists in modifying image spectrum in order to obtain an image in which we will be able to detect edges of objects. The second method, based on region segmentation, has been developed from background subtraction methods. The background image has been learned at the beginning of the video when there is no object. The results of the latter have been compared to those obtained with a state-of-art method, on data acquired at sea. Once we have detected an object, we want to recognize it. For that, we use the correlation technique. The reference images have been obtained from 3D computer generated images of mines. This novel approach gives promising results. For each developed method, we have optimized the results through the use of navigational information. Indeed, depending on vehicle's motion, we can set constraints to improve the detection step and reduce processing time.

Minor utgör ett hot mot civilbefolkningen men även personal i fredsfrämjande insatser riskerar att skadas av minorna under patruller i insatsområdet. Idag används minhunden i Försvarsmakten för att lokaliera minor i kombination med minpik och metalldetektor. Hunden har sina begränsningar och har under flera perioder varit på väg att fasas ut till förmån för tekniken. Minans doftbild, minhunden och den elektroniska nosen beskrivs och mynnar ut i en analys där för-/nackdelar presenteras och därefter diskuteras. Den militära nyttan i den militära kontexten, insats med undsättningsstyrka (MRT) utgör ramverk i uppsatsen. Vid en undsättningsinsats med MRT är det av största vikt att minorna kan lokaliseras, märkas ut och undvikas. Hunden har förmåga till lokalisering av minor vilket den elektroniska nosen Fido saknar. Minornas doftbild överlappar varandra. Fido kan inte särskilja minorna från varandra utan endast bekräfta förekomst i ett område vilket inte bidrar till den militära nyttan när en fri väg ska sökas fram till en skadeplats. / Landmines pose a threat to the civilian population but personnel in peacekeeping operations could also be harmed by landmines during patrols in the area. Today the Swedish Armed Forces use mine detection dogs to locate landmines. The mine detection dogs are used in combination with prodders and metal detectors. The dogs have their limitations and there have been several attempts to phase them out and replace them with technology. Substances detected by dogs and electronic noses, the mine detection dog and the electronic dog nose Fido are described and incorporated in an analysis where advantages/disadvantages are presented and then discussed. Military utility in a military context constitute the frame of the essay. The military context is an operation with a mine rescue team to rescue an injured person in a mine field. In a rescue operation with a mine rescue team the most important thing is to locate, mark and avoid the landmines. The dog can locate landmines but the electronic nose Fido cannot. The chemical signatures from the landmines overlap each other making it difficult for Fido to pinpoint the exact location. Fido can confirm the presence of landmines in an area which does not have military utility during mine rescue team operations to find a free path and rescue an injured person out from a minefield.

No description available.