Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

Anti-predator adaptations in aquatic environments

Predation is an important selective force that has led to the evolution of a variety of fascinating anti-predator adaptations, such as many types of protective coloration and prey behaviours. Because the evolution of life has begun in the aquatic environment and many anti-predator adaptations are found already in relative primitive taxa, it is likely that many of these adaptations evolved initially in the aquatic environment. Yet, there has been surprisingly little research on the mechanisms and function of antipredator adaptations in aquatic systems. To understand the function of anti-predator adaptations and natural selection imposed on prey appearance and behaviour, I have investigated how protective coloration can be used, either as such or together with behavioural adaptations, to manipulate predator behaviour and decrease predation risk. To this end I conducted a series of behaviour ecological laboratory experiments in which I manipulated the visual appearance of artificial backgrounds and prey items. In paper I of this thesis, I investigated background choice as an anti-predator strategy, by observing the habitat choice of the least killifish (Heterandria formosa) between pairs of artificial backgrounds, both in the presence and absence of predation threat. It has been suggested that prey could decrease their risk of being detected by predators either by preferring backgrounds into which they blend or by preferring visually complex backgrounds. The least killifish preferred a background that matched their patterning to a background that mismatched it, showing that they are able to respond to cues of visual similarity between their colour pattern and the surrounding environment. Interestingly however, in female least killifish visual complexity of the background was a more important cue for habitat safety and may override or act together with background matching when searching for a safe habitat. It is possible that in females, preference for visually complex backgrounds is associated with lower opportunity costs than preference for matching backgrounds would be. Generally, the least killifish showed stronger preference while under predation threat, indicating that their background choice behaviour is an antipredator adaptation. Many aquatic prey species have eyespots, which are colour patterns that consist of roughly concentric rings and have received their name because they for humans often resemble the vertebrate eye. I investigated the anti-predator function of eyespots against predation by fish in papers II, III and IV. Some eyespots have been suggested to benefit prey by diverting the strikes of predators away from vital parts of the prey body or towards a direction that facilitates prey escape. Although proposed over a century ago, the divertive effect of eyespots has proven to be difficult to show experimentally. In papers II and III, I tested for divertive effect of eyespots towards attacking fish by presenting artificial prey with eyespots to laboratory reared three-spined sticklebacks (Gasterosteus aculeatus). I found that eyespots strongly influenced the behaviour of attacking sticklebacks and effectively drew their strikes towards the eyespots. To further investigate this divertive effect and whether the specific shape of eyespots is important for it, I tested in paper III the response of fish also to other markings than eyespots. I found that eyespots were generally more effective in diverting the first strikes of attacking fish compared to other prey markings. My findings suggest that the common occurrence of eyespots in aquatic prey species can at least partly be explained by the divertive effect of the eyespot shape, possibly together with the relative simple developmental mechanisms underlying circular colour patterns. An eyebar is a stripe that runs through the eye, and this pattern has been suggested to obscure the real eyes of the prey by visually blending parts of the eyes and head of the prey and by creating false edges. In paper III, I show that an eyebar effectively disrupts an eyelike shape. This suggests that eyebars provide an effective way to conceal the eyes and consequently obstruct detection and recognition of prey. This experiment also demonstrates that through concealment of the eyes, eyebars could be used to enhance the divertive effect of eyespots, which can explain the common occurrence of eyebars in many species of fish that have eyespots. Larger eyespots have been shown to intimidate some terrestrial predators, such as passerine birds, either because they resemble the eyes of the predator’s own enemy or because highly salient features may have an intimidating effect. In papers II and IV, I investigated whether the occurrence of eyespots in some aquatic prey could be explained by their intimidating effect predatory fish. In paper IV, I also investigated the reason for the intimidating effect of eyelike prey marks. In paper II, I found no clear intimidating effect of eyespots, whereas in paper IV, using a different approach, I found that sticklebacks hesitated to attack towards eyelike but not towards non-eyelike marks. Importantly, paper IV therefore presents the first rigorous evidence for the idea that eye mimicry, and not merely conspicuousness, underlies the intimidating effect. It also showed that the hesitation shown by fish towards eyelike marks is partly an innate response that is reinforced by encounters with predators. Collectively, this thesis shows that prey colour pattern and the visual appearance of the habitat influence the behaviour of fish. The results demonstrate that protective coloration provides numerous distinctive ways for aquatic prey to escape predation. Thus, visual perception and behaviour of fish are important factors shaping the appearance and behaviours of aquatic prey.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Retinal Image Analysis Using Machine Vision

The topic of this thesis is studying how lesions in retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. Methods for equalizing uneven illumination in fundus images, detecting regions of poor image quality due toinadequate illumination, and recognizing abnormal lesions were developed duringthe work. The developed methods exploit mainly the color information and simpleshape features to detect lesions. In addition, a graphical tool for collecting lesion data was developed. The tool was used by an ophthalmologist who marked lesions in the images to help method development and evaluation. The tool is a general purpose one, and thus it is possible to reuse the tool in similar projects.The developed methods were tested with a separate test set of 128 color fundus images. From test results it was calculated how accurately methods classify abnormal funduses as abnormal (sensitivity) and healthy funduses as normal (specificity). The sensitivity values were 92% for hemorrhages, 73% for red small dots (microaneurysms and small hemorrhages), and 77% for exudates (hard and soft exudates). The specificity values were 75% for hemorrhages, 70% for red small dots, and 50% for exudates. Thus, the developed methods detected hemorrhages accurately and microaneurysms and exudates moderately.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Similarity measure based classification

Luokittelujärjestelmää suunniteltaessa tarkoituksena on rakentaa systeemi, joka pystyy ratkaisemaan mahdollisimman tarkasti tutkittavan ongelma-alueen. Hahmontunnistuksessa tunnistusjärjestelmän ydin on luokitin. Luokittelun sovellusaluekenttä on varsin laaja. Luokitinta tarvitaan mm. hahmontunnistusjärjestelmissä, joista kuvankäsittely toimii hyvänä esimerkkinä. Myös lääketieteen parissa tarkkaa luokittelua tarvitaan paljon. Esimerkiksi potilaan oireiden diagnosointiin tarvitaan luokitin, joka pystyy mittaustuloksista päättelemään mahdollisimman tarkasti, onko potilaalla kyseinen oire vai ei. Väitöskirjassa on tehty similaarisuusmittoihin perustuva luokitin ja sen toimintaa on tarkasteltu mm. lääketieteen paristatulevilla data-aineistoilla, joissa luokittelutehtävänä on tunnistaa potilaan oireen laatu. Väitöskirjassa esitetyn luokittimen etuna on sen yksinkertainen rakenne, josta johtuen se on helppo tehdä sekä ymmärtää. Toinen etu on luokittimentarkkuus. Luokitin saadaan luokittelemaan useita eri ongelmia hyvin tarkasti. Tämä on tärkeää varsinkin lääketieteen parissa, missä jo pieni tarkkuuden parannus luokittelutuloksessa on erittäin tärkeää. Väitöskirjassa ontutkittu useita eri mittoja, joilla voidaan mitata samankaltaisuutta. Mitoille löytyy myös useita parametreja, joille voidaan etsiä juuri kyseiseen luokitteluongelmaan sopivat arvot. Tämä parametrien optimointi ongelma-alueeseen sopivaksi voidaan suorittaa mm. evoluutionääri- algoritmeja käyttäen. Kyseisessä työssä tähän on käytetty geneettistä algoritmia ja differentiaali-evoluutioalgoritmia. Luokittimen etuna on sen joustavuus. Ongelma-alueelle on helppo vaihtaa similaarisuusmitta, jos kyseinen mitta ei ole sopiva tutkittavaan ongelma-alueeseen. Myös eri mittojen parametrien optimointi voi parantaa tuloksia huomattavasti. Kun käytetään eri esikäsittelymenetelmiä ennen luokittelua, tuloksia pystytään parantamaan.

Keraamisten laattojen luokittelu väripiirteiden avulla

Vaikka keraamisten laattojen valmistusprosessi onkin täysin automatisoitu, viimeinen vaihe eli laaduntarkistus ja luokittelu tehdään yleensä ihmisvoimin. Automaattinen laaduntarkastus laattojen valmistuksessa voidaan perustella taloudellisuus- ja turvallisuusnäkökohtien avulla. Tämän työn tarkoituksena on kuvata tutkimusprojektia keraamisten laattojen luokittelusta erilaisten väripiirteiden avulla. Oleellisena osana tutkittiin RGB- ja spektrikuvien välistä eroa. Työn teoreettinen osuus käy läpi aiemmin aiheesta tehdyn tutkimuksen sekä antaa taustatietoa konenäöstä, hahmontunnistuksesta, luokittelijoista sekä väriteoriasta. Käytännön osan aineistona oli 25 keraamista laattaa, jotka olivat viidestä eri luokasta. Luokittelussa käytettiin apuna k:n lähimmän naapurin (k-NN) luokittelijaa sekä itseorganisoituvaa karttaa (SOM). Saatuja tuloksia verrattiin myös ihmisten tekemään luokitteluun. Neuraalilaskenta huomattiin tärkeäksi työkaluksi spektrianalyysissä. SOM:n ja spektraalisten piirteiden avulla saadut tulokset olivat lupaavia ja ainoastaan kromatisoidut RGB-piirteet olivat luokittelussa parempia kuin nämä.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4

Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

Oliopohjainen konenäkösovelluskehys

Monet teollisuuden konenäkö- ja hahmontunnistusongelmat ovat hyvin samantapaisia, jolloin prototyyppisovelluksia suunniteltaessa voitaisiin hyödyntää pitkälti samoja komponentteja. Oliopohjaiset sovelluskehykset tarjoavat erinomaisen tavan nopeuttaa ohjelmistokehitystä uudelleenkäytettävyyttä parantamalla. Näin voidaan sekä mahdollistaa konenäkösovellusten laajempi käyttö että säästää kustannuksissa. Tässä työssä esitellään konenäkösovelluskehys, joka on perusarkkitehtuuriltaan liukuhihnamainen. Ylätason rakenne koostuu sensorista, datankäsittelyoperaatioista, piirreirrottimesta sekä luokittimesta. Itse sovelluskehyksen lisäksi on toteutettu joukko kuvankäsittely- ja hahmontunnistusoperaatioita. Sovelluskehys nopeuttaa selvästi ohjelmointityötä ja helpottaa uusien kuvankäsittelyoperaatioiden lisää mistä.

CFD Modelling of Direct Contact Condensation in Suppression Pools by Applying Condensation Models of Separated Flow

The condensation rate has to be high in the safety pressure suppression pool systems of Boiling Water Reactors (BWR) in order to fulfill their safety function. The phenomena due to such a high direct contact condensation (DCC) rate turn out to be very challenging to be analysed either with experiments or numerical simulations. In this thesis, the suppression pool experiments carried out in the POOLEX facility of Lappeenranta University of Technology were simulated. Two different condensation modes were modelled by using the 2-phase CFD codes NEPTUNE CFD and TransAT. The DCC models applied were the typical ones to be used for separated flows in channels, and their applicability to the rapidly condensing flow in the condensation pool context had not been tested earlier. A low Reynolds number case was the first to be simulated. The POOLEX experiment STB-31 was operated near the conditions between the ’quasi-steady oscillatory interface condensation’ mode and the ’condensation within the blowdown pipe’ mode. The condensation models of Lakehal et al. and Coste & Lavi´eville predicted the condensation rate quite accurately, while the other tested ones overestimated it. It was possible to get the direct phase change solution to settle near to the measured values, but a very high resolution of calculation grid was needed. Secondly, a high Reynolds number case corresponding to the ’chugging’ mode was simulated. The POOLEX experiment STB-28 was chosen, because various standard and highspeed video samples of bubbles were recorded during it. In order to extract numerical information from the video material, a pattern recognition procedure was programmed. The bubble size distributions and the frequencies of chugging were calculated with this procedure. With the statistical data of the bubble sizes and temporal data of the bubble/jet appearance, it was possible to compare the condensation rates between the experiment and the CFD simulations. In the chugging simulations, a spherically curvilinear calculation grid at the blowdown pipe exit improved the convergence and decreased the required cell count. The compressible flow solver with complete steam-tables was beneficial for the numerical success of the simulations. The Hughes-Duffey model and, to some extent, the Coste & Lavi´eville model produced realistic chugging behavior. The initial level of the steam/water interface was an important factor to determine the initiation of the chugging. If the interface was initialized with a water level high enough inside the blowdown pipe, the vigorous penetration of a water plug into the pool created a turbulent wake which invoked the chugging that was self-sustaining. A 3D simulation with a suitable DCC model produced qualitatively very realistic shapes of the chugging bubbles and jets. The comparative FFT analysis of the bubble size data and the pool bottom pressure data gave useful information to distinguish the eigenmodes of chugging, bubbling, and pool structure oscillations.

Kahden merkkijonon pisimmän yhteisen alijonon ongelma ja sen ratkaiseminen

Tämä tutkielma kuuluu merkkijonoalgoritmiikan piiriin. Merkkijono S on merkkijonojen X[1..m] ja Y[1..n] yhteinen alijono, mikäli se voidaan muodostaa poistamalla X:stä 0..m ja Y:stä 0..n kappaletta merkkejä mielivaltaisista paikoista. Jos yksikään X:n ja Y:n yhteinen alijono ei ole S:ää pidempi, sanotaan, että S on X:n ja Y:n pisin yhteinen alijono (lyh. PYA). Tässä työssä keskitytään kahden merkkijonon PYAn ratkaisemiseen, mutta ongelma on yleistettävissä myös useammalle jonolle. PYA-ongelmalle on sovelluskohteita – paitsi tietojenkäsittelytieteen niin myös bioinformatiikan osa-alueilla. Tunnetuimpia niistä ovat tekstin ja kuvien tiivistäminen, tiedostojen versionhallinta, hahmontunnistus sekä DNA- ja proteiiniketjujen rakennetta vertaileva tutkimus. Ongelman ratkaisemisen tekee hankalaksi ratkaisualgoritmien riippuvuus syötejonojen useista eri parametreista. Näitä ovat syötejonojen pituuden lisäksi mm. syöttöaakkoston koko, syötteiden merkkijakauma, PYAn suhteellinen osuus lyhyemmän syötejonon pituudesta ja täsmäävien merkkiparien lukumäärä. Täten on vaikeaa kehittää algoritmia, joka toimisi tehokkaasti kaikille ongelman esiintymille. Tutkielman on määrä toimia yhtäältä käsikirjana, jossa esitellään ongelman peruskäsitteiden kuvauksen jälkeen jo aikaisemmin kehitettyjä tarkkoja PYAalgoritmeja. Niiden tarkastelu on ryhmitelty algoritmin toimintamallin mukaan joko rivi, korkeuskäyrä tai diagonaali kerrallaan sekä monisuuntaisesti prosessoiviin. Tarkkojen menetelmien lisäksi esitellään PYAn pituuden ylä- tai alarajan laskevia heuristisia menetelmiä, joiden laskemia tuloksia voidaan hyödyntää joko sellaisinaan tai ohjaamaan tarkan algoritmin suoritusta. Tämä osuus perustuu tutkimusryhmämme julkaisemiin artikkeleihin. Niissä käsitellään ensimmäistä kertaa heuristiikoilla tehostettuja tarkkoja menetelmiä. Toisaalta työ sisältää laajahkon empiirisen tutkimusosuuden, jonka tavoitteena on ollut tehostaa olemassa olevien tarkkojen algoritmien ajoaikaa ja muistinkäyttöä. Kyseiseen tavoitteeseen on pyritty ohjelmointiteknisesti esittelemällä algoritmien toimintamallia hyvin tukevia tietorakenteita ja rajoittamalla algoritmien suorittamaa tuloksetonta laskentaa parantamalla niiden kykyä havainnoida suorituksen aikana saavutettuja välituloksia ja hyödyntää niitä. Tutkielman johtopäätöksinä voidaan yleisesti todeta tarkkojen PYA-algoritmien heuristisen esiprosessoinnin lähes systemaattisesti pienentävän niiden suoritusaikaa ja erityisesti muistintarvetta. Lisäksi algoritmin käyttämällä tietorakenteella on ratkaiseva vaikutus laskennan tehokkuuteen: mitä paikallisempia haku- ja päivitysoperaatiot ovat, sitä tehokkaampaa algoritmin suorittama laskenta on.

Stop bugging me! : diversity in appearance of warning coloration

In nature, many animals use body coloration to communicate with each other. For example, colorations can be used as signals between individuals of the same species, but also to recognise individuals of other species, and if they may comprise a threat or not. Many animals use protective coloration to avoid predation. The two most common strategies of protective coloration are camouflage and aposematism. Camouflaged animals have coloration that minimises detection, usually by matching colours or structures in the background. Aposematic animals, on the other hand, signal to predators that they are defended. The defence can be physical structures, such as spikes and hairs, or chemical compounds that make the animal distasteful or even deadly toxic. In order for the warning signal to be effective, the predator has to recognise it as such. Studies have shown that birds for example, that are important visual predators on insects, learn to recognise and avoid unpalatable prey faster if they contrast the background or have large internal contrasts. Typical examples of aposematic species have conspicuous colours like yellow, orange or red, often in combination with black. My thesis focuses on the appearance and function of aposematic colour patterns. Even though researchers have studied aposematism for over a century, there is still a lot we do not know about the phenomenon. For example, as it is crucial that the predators recognise a warning signal, aposematic colorations should assumingly evolve homogeneously and be selected for maximal conspicuousness. Instead, there is an extensive variation of colours and patterns among warning colorations, and it is not uncommon to find typical cryptic colours, such as green and brown in aposematic colour patterns. One hypothesis to this variation is that an aposematic coloration does not have to be maximally signalling in order to be effective, instead it is sufficient to have distinct features that can be easily distinguished from edible prey. To be maximally conspicuous is one way to achieve this, but not the only way. Another hypothesis is that aposematic prey that do not exhibit maximal conspicuousness can exploit both camouflage and aposematism in a distance-dependent fashion, by being signalling when seen close up but camouflaged at a distance. Many prey animals also make use of both strategies by shifting colour at different ecological conditions such as seasonal variations, fluctuations in food resources or between life stages. Yet another explanation for the variation may be that prey animals are usually exposed to several predator species that vary in visual perception and tolerance towards various toxins. The aim with this thesis is, by studying their functions, to understand why aposematic warning signals vary in appearance, specifically in the level of conspicuousness, and if warning coloration can be combined with camouflage. In paper I, I investigated if the colour pattern of the aposematic larva of the Apollo butterfly (Parnassius apollo) can switch function with viewing distance, and be signalling at close range but camouflaged at a distance, by comparing detection time between different colour variants and distances. The results show that the natural coloration has a dual distance-dependent function. Moreover, the study shows that an aposematic coloration does not have to be selected for maximal conspicuousness. A prey animal can optimise its coloration primarily by avoiding detection, but also by investing in a secondary defence, which presence can be signalled if detected. In paper II, I studied how easily detected the coloration of the firebug (Pyrrhocoris apterus), a typical aposematic species, is at different distances against different natural backgrounds, by comparing detection time between different colour variants. Here, I found no distance-dependent switch in function. Instead, the results show that the coloration of the firebug is selected for maximal conspicuousness. One explanation for this is that the firebug is more mobile than the butterfly larva in study I, and movement is often incompatible with efficient camouflage. In paper III, I investigated if a seasonal related colour change in the chemically defended striated shieldbug (Graphosoma lineatum) is an adaptation to optimise a protective coloration by shifting from camouflage to aposematism between two seasons. The results confirm the hypothesis that the coloration expressed in the late summer has a camouflage function, blending in with the background. Further, I investigated if the internal pattern as such increased the effectiveness of the camouflage. Again, the results are in accordance with the hypothesis, as the patterned coloration was more difficult to detect than colorations lacking an internal pattern. This study shows how an aposematic species can optimise its defence by shifting from camouflage to aposematism, but in a different fashion than studied in paper I. The aim with study IV was to study the selection on aposematic signals by identifying characteristics that are common for colorations of aposematic species, and that distinguish them from colorations of other species. I compared contrast, pattern element size and colour proportion between a group of defended species and a group of undefended species. In contrast to my prediction, the results show no significant differences between the two groups in any of the analyses. One explanation for the non-significant results could be that there are no universal characteristics common for aposematic species. Instead, the selection pressures acting on defended species vary, and therefore affect their appearance differently. Another explanation is that all defended species may not have been selected for a conspicuous aposematic warning coloration. Taken together, my thesis shows that having a conspicuous warning coloration is not the only way to be aposematic. Also, aposematism and camouflage is not two mutually exclusive opposites, as there are prey species that exploit both strategies. It is also important to understand that prey animals are exposed to various selection pressures and trade-offs that affect their appearance, and determines what an optimal coloration is for each species or environment. In conclusion, I hold that the variation among warning colorations is larger and coloration properties that have been considered as archetypically aposematic may not be as widespread and representative as previously assumed.

Local Features in Image and Video Processing - Object Class Matching and Video Shot Detection

The usage of digital content, such as video clips and images, has increased dramatically during the last decade. Local image features have been applied increasingly in various image and video retrieval applications. This thesis evaluates local features and applies them to image and video processing tasks. The results of the study show that 1) the performance of different local feature detector and descriptor methods vary significantly in object class matching, 2) local features can be applied in image alignment with superior results against the state-of-the-art, 3) the local feature based shot boundary detection method produces promising results, and 4) the local feature based hierarchical video summarization method shows promising new new research direction. In conclusion, this thesis presents the local features as a powerful tool in many applications and the imminent future work should concentrate on improving the quality of the local features.