Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

The role of auditory cortices in the retrieval of single-trial auditory-visual object memories.

Single-trial encounters with multisensory stimuli affect both memory performance and early-latency brain responses to visual stimuli. Whether and how auditory cortices support memory processes based on single-trial multisensory learning is unknown and may differ qualitatively and quantitatively from comparable processes within visual cortices due to purported differences in memory capacities across the senses. We recorded event-related potentials (ERPs) as healthy adults (n = 18) performed a continuous recognition task in the auditory modality, discriminating initial (new) from repeated (old) sounds of environmental objects. Initial presentations were either unisensory or multisensory; the latter entailed synchronous presentation of a semantically congruent or a meaningless image. Repeated presentations were exclusively auditory, thus differing only according to the context in which the sound was initially encountered. Discrimination abilities (indexed by d') were increased for repeated sounds that were initially encountered with a semantically congruent image versus sounds initially encountered with either a meaningless or no image. Analyses of ERPs within an electrical neuroimaging framework revealed that early stages of auditory processing of repeated sounds were affected by prior single-trial multisensory contexts. These effects followed from significantly reduced activity within a distributed network, including the right superior temporal cortex, suggesting an inverse relationship between brain activity and behavioural outcome on this task. The present findings demonstrate how auditory cortices contribute to long-term effects of multisensory experiences on auditory object discrimination. We propose a new framework for the efficacy of multisensory processes to impact both current multisensory stimulus processing and unisensory discrimination abilities later in time.

XML-based content transformation in converged service development

Tele- ja dataviestinnän yhdistyminen digitaaliseen sisältöön luo uusia palveluideoita sekä mobiili- että internetverkkoihin. Nämä palvelut kehitetään usein erikseen, jolloin saman sisällön käyttäminen eri pääsymenetelmin ei ole mahdollista. Sisältömuunnos on mahdollista sisällön ja muotoilun eriyttämisellä, joka puolestaan vaatii informaatioyksiköiden merkkauksen sisältöä kuvaavilla lisätiedoilla. Tässä diplomityössä tutkitaan Extensible Markup Languagen (XML) käyttöä yhdistyneiden palvelujen sisältömuunnoksessa. Nykyisiä ja tulevia palveluita ja verkkoja tarkastellaan sekä sisällön että liiketoiminnan kannalta. Lisäksi esitellään lyhyesti omia ajatuksia ja käsityksiä yhdistyneistä palveluista ja informaation täsmällisyydestä. Työn käytännön osuudessa kuvataan itse suunniteltu palvelualusta sekä esitellään sen avulla rakennettuja sovelluksia

Managing R&D Content with Web-based KM Software

Työssä tutkittiin tehokasta tietojohtamista globaalin metsäteollisuusyrityksen tutkimus ja kehitys verkostossa. Työn tavoitteena oli rakentaa kuvaus tutkimus ja kehitys sisällön hallintaan kohdeyrityksen käyttämän tietojohtamisohjelmiston avulla. Ensin selvitettiin käsitteitä tietämys ja tietojohtaminen kirjallisuuden avulla. Selvityksen perusteella esitettiin prosessimalli, jolla tietämystä voidaan tehokkaasti hallita yrityksessä. Seuraavaksi analysoitiin tietojohtamisen asettamia vaatimuksia informaatioteknologialle ja informaatioteknologian roolia prosessimallissa. Verkoston vaatimukset tietojohtamista kohtaan selvitettiin haastattelemalla yrityksen avainhenkilöitä. Haastatteluiden perusteella järjestelmän tuli tehokkaasti tukea virtuaalisten projektiryhmien työskentelyä, mahdollistaa tehtaiden välinen tietämyksen jakaminen ja tukea järjestelmään syötetyn sisällön hallintaa. Ensiksi järjestelmän käyttöliittymän rakenne ja salaukset muokattiin vastaamaan verkoston tarpeita. Rakenne tarjoaa työalueen työryhmille ja alueet tehtaiden väliseen tietämyksen jakamiseen. Sisällönhallintaa varten järjestelmään kehitettiin kategoria, profiloitu portaali ja valmiiksi määriteltyjä hakuja. Kehitetty malli tehostaa projektiryhmien työskentelyä, mahdollistaa olemassa olevan tietämyksen hyväksikäytön tehdastasolla sekä helpottaa tutkimus ja kehitys aktiviteettien seurantaa. Toimenpide-ehdotuksina esitetään järjestelmän integrointia tehtaiden operatiivisiin ohjausjärjestelmiin ja ohjelmiston käyttöönottoa tehdastason projektinhallinta työkaluksi.Ehdotusten tavoitteena on varmistaa sekä tehokas tietämyksen jakaminen tehtaiden välillä että tehokas tietojohtaminen tehdastasolla.

Inter-rater Reliability of Criteria-Based Content Analysis of Children's Statements of Abuse.

The evaluation of children's statements of sexual abuse cases in forensic cases is critically important and must and reliable. Criteria-based content analysis (CBCA) is the main component of the statement validity assessment (SVA), which is the most frequently used approach in this setting. This study investigated the inter-rater reliability (IRR) of CBCA in a forensic context. Three independent raters evaluated the transcripts of 95 statements of sexual abuse. IRR was calculated for each criterion, total score, and overall evaluation. The IRR was variable for the criteria, with several being unsatisfactory. But high IRR was found for the total CBCA scores (Kendall's W = 0.84) and for overall evaluation (Kendall's W = 0.65). Despite some shortcomings, SVA remains a robust method to be used in the comprehensive evaluation of children's statements of sexual abuse in the forensic setting. However, the low IRR of some CBCA criteria could justify some technical improvements.

Managing induced tourism image : relational patterns and the life cycle

The tourism image is an element that conditions the competitiveness of tourism destinations by making them stand out in the minds of tourists. In this context, marketers of tourism destinations endeavour to create an induced image based on their identity and distinctive characteristics.A number of authors have also recognized the complexity of tourism destinations and the need for coordination and cooperation among all tourism agents, in order to supply a satisfactory tourist product and be competitive in the tourism market. Therefore, tourism agents at the destination need to develop and integrate strategic marketing plans.The aim of this paper is to determine how cities of similar cultures use their resources with the purpose of developing a distinctive induced tourism image to attract tourists and the extent of coordination and cooperation among the various tourism agents of a destination in the process of induced image creation.In order to accomplish these aims, a comparative analysis of the induced image of two cultural cities is presented, Girona (Spain) and Perpignan (France). The induced image is assessed through the content analysis of promotional brochures and the extent of cooperation with in-depth interviews of the main tourism agents of these destinations.Despite the similarities of both cities in terms of tourism resources, results show the use of different attributes to configure the induced image of each destination, as well as a different configuration of the network of tourism agents that participate in the process of induced image creation

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Characterisation of pigment particles by scanning electron microscope and image analysis programs

Coating and filler pigments have strong influence to the properties of the paper. Filler content can be even over 30 % and pigment content in coating is about 85-95 weight percent. The physical and chemical properties of the pigments are different and the knowledge of these properties is important for optimising of optical and printing properties of the paper. The size and shape of pigment particles can be measured by different analysers which can be based on sedimentation, laser diffraction, changes in electric field etc. In this master's thesis was researched particle properties especially by scanning electron microscope (SEM) and image analysis programs. Research included nine pigments with different particle size and shape. Pigments were analysed by two image analysis programs (INCA Feature and Poikki), Coulter LS230 (laser diffraction) and SediGraph 5100 (sedimentation). The results were compared to perceive the effect of particle shape to the performance of the analysers. Only image analysis programs gave parameters of the particle shape. One part of research was also the sample preparation for SEM. Individual particles should be separated and distinct in ideal sample. Analysing methods gave different results but results from image analysis programs corresponded even to sedimentation or to laser diffraction depending on the particle shape. Detailed analysis of the particle shape required high magnification in SEM, but measured parameters described very well the shape of the particles. Large particles (ecd~1 µm) could be used also in 3D-modelling which enabled the measurement of the thickness of the particles. Scanning electron microscope and image analysis programs were effective and multifunctional tools for particle analyses. Development and experience will devise the usability of analysing method in routine use.

Neural correlates of language learning in adults

The human language-learning ability persists throughout life, indicating considerable flexibility at the cognitive and neural level. This ability spans from expanding the vocabulary in the mother tongue to acquisition of a new language with its lexicon and grammar. The present thesis consists of five studies that tap both of these aspects of adult language learning by using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) during language processing and language learning tasks. The thesis shows that learning novel phonological word forms, either in the native tongue or when exposed to a foreign phonology, activates the brain in similar ways. The results also show that novel native words readily become integrated in the mental lexicon. Several studies in the thesis highlight the left temporal cortex as an important brain region in learning and accessing phonological forms. Incidental learning of foreign phonological word forms was reflected in functionally distinct temporal lobe areas that, respectively, reflected short-term memory processes and more stable learning that persisted to the next day. In a study where explicitly trained items were tracked for ten months, it was found that enhanced naming-related temporal and frontal activation one week after learning was predictive of good long-term memory. The results suggest that memory maintenance is an active process that depends on mechanisms of reconsolidation, and that these process vary considerably between individuals. The thesis put special emphasis on studying language learning in the context of language production. The neural foundation of language production has been studied considerably less than that of perceptive language, especially on the sentence level. A well-known paradigm in language production studies is picture naming, also used as a clinical tool in neuropsychology. This thesis shows that accessing the meaning and phonological form of a depicted object are subserved by different neural implementations. Moreover, a comparison between action and object naming from identical images indicated that the grammatical class of the retrieved word (verb, noun) is less important than the visual content of the image. In the present thesis, the picture naming was further modified into a novel paradigm in order to probe sentence-level speech production in a newly learned miniature language. Neural activity related to grammatical processing did not differ between the novel language and the mother tongue, but stronger neural activation for the novel language was observed during the planning of the upcoming output, likely related to more demanding lexical retrieval and short-term memory. In sum, the thesis aimed at examining language learning by combining different linguistic domains, such as phonology, semantics, and grammar, in a dynamic description of language processing in the human brain.

Real-time Image-based RGB-D Camera Motion Tracking and Environment Mapping

In this work, image based estimation methods, also known as direct methods, are studied which avoid feature extraction and matching completely. Cost functions use raw pixels as measurements and the goal is to produce precise 3D pose and structure estimates. The cost functions presented minimize the sensor error, because measurements are not transformed or modified. In photometric camera pose estimation, 3D rotation and translation parameters are estimated by minimizing a sequence of image based cost functions, which are non-linear due to perspective projection and lens distortion. In image based structure refinement, on the other hand, 3D structure is refined using a number of additional views and an image based cost metric. Image based estimation methods are particularly useful in conditions where the Lambertian assumption holds, and the 3D points have constant color despite viewing angle. The goal is to improve image based estimation methods, and to produce computationally efficient methods which can be accomodated into real-time applications. The developed image-based 3D pose and structure estimation methods are finally demonstrated in practise in indoor 3D reconstruction use, and in a live augmented reality application.

Model-Based Tracking Initialization in Ship Building Environment

An augmented reality (AR) device must know observer’s location and orientation, i.e. observer’s pose, to be able to correctly register the virtual content to observer’s view. One possible way to determine and continuously follow-up the pose is model-based visual tracking. It supposes that a 3D model of the surroundings is known and that there is a video camera that is fixed to the device. The pose is tracked by comparing the video camera image to the model. Each new pose estimate is usually based on the previous estimate. However, the first estimate must be found out without a prior estimate, i.e. the tracking must be initialized, which in practice means that some model features must be identified from the image and matched to model features. This is known in literature as model-to-image registration problem or simultaneous pose and correspondence problem. This report reviews visual tracking initialization methods that are suitable for visual tracking in ship building environment when the ship CAD model is available. The environment is complex, which makes the initialization non-trivial. The report has been done as part of MARIN project.

The Role of Video Based Content Marketing as a Part of Modern Marketing Communications

The thesis studies the role of video based content marketing as a part of modern marketing communications.

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

Digital Content Marketing in the Business-to-Business Context. A Case Study on Marketing Communications Agencies.

Marketing has changed because of digitalization. Marketing is moving towards digital channels and more companies are transitioning from “pushing” advertising messages to “pull” marketing, that attracts audience with the content that interests and benefits the audience. This kind of marketing is called content marketing or “inbound” marketing. This study focuses on how marketing communications agencies utilize digital content marketing and what are the best practices with the selected digital content marketing channels. In this study, those channels include blogs, Facebook, Twitter, and LinkedIn. The qualitative research method was utilized in order to examine the phenomenon of digital content marketing in-depth. The chosen data collecting method was semi-structured interviewing. A total of seven marketing communications agencies, who currently utilize digital content marketing, were selected as case companies and interviewed. All the case companies are from the marketing communications industry because that industry can be assumed to be well adapted to digital content marketing techniques. There is a research gap about digital content marketing in the B2B context, which increases the novelty value of this research. The study examines what is digital content marketing, why B2B companies use digital content marketing, and how should digital content marketing be conducted through blogs and social media. The informants perceived digital marketing to be a fundamental part of their all marketing. They conduct digital content marketing for the following reasons: to increase sales, to improve their brand image and to demonstrate their own skills. Concrete results of digital content marketing for the case companies include sales leads, new clients, better brand image, and that recruiting is easier. The most important success factors with blogs and social media are the following: 1) Audience-centric thinking. All content planning should start from figuring out which themes interests the target audience. Social media channel choices should be based on where the target audience can be reached. 2) Companies should not talk only about themselves. Instead, content is made about themes that interests the target audience. On social media channels, only a fragment of all shared content is about the company. Rather, most of the shared content is industry-specific content that helps the potential client.