Modelling and segmentation of the vocal tract during speech production by using deformable models in magnetic resonance images

The first and second authors would like to thank the support of the PhD grants with references SFRH/BD/28817/2006 and SFRH/PROTEC/49517/2009, respectively, from Fundação para a Ciência e Tecnol ogia (FCT). This work was partially done in the scope of the project “Methodologies to Analyze Organs from Complex Medical Images – Applications to Fema le Pelvic Cavity”, wi th reference PTDC/EEA- CRO/103320/2008, financially supported by FCT.

Building the View Graph of a Category by Exploiting Image Realism

We propose a weakly supervised method to arrange images of a given category based on the relative pose between the camera and the object in the scene. Relative poses are points on a sphere centered at the object in a given canonical pose, which we call object viewpoints. Our method builds a graph on this sphere by assigning images with similar viewpoint to the same node and by connecting nodes if they are related by a small rotation. The key idea is to exploit a large unlabeled dataset to validate the likelihood of dominant 3D planes of the object geometry. A number of 3D plane hypotheses are evaluated by applying small 3D rotations to each hypothesis and by measuring how well the deformed images match other images in the dataset. Correct hypotheses will result in deformed images that correspond to plausible views of the object, and thus will likely match well other images in the same category. The identified 3D planes are then used to compute affinities between images related by a change of viewpoint. We then use the affinities to build a view graph via a greedy method and the maximum spanning tree.

The role of context in image annotation and recommendation

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).

Multi-temporal analysis of radiometric changes in satellite images of forest edges to infer selective-logging areas in the Amazon forest

Radiometric changes observed in multi-temporal optical satellite images have an important role in efforts to characterize selective-logging areas. The aim of this study was to analyze the multi-temporal behavior of spectral-mixture responses in satellite images in simulated selective-logging areas in the Amazon forest, considering red/near-infrared spectral relationships. Forest edges were used to infer the selective-logging infrastructure using differently oriented edges in the transition between forest and deforested areas in satellite images. TM/Landsat-5 images acquired at three dates with different solar-illumination geometries were used in this analysis. The method assumed that the radiometric responses between forest with selective-logging effects and forest edges in contact with recent clear-cuts are related. The spatial frequency attributes of red/near infrared bands for edge areas were analyzed. Analysis of dispersion diagrams showed two groups of pixels that represent selective-logging areas. The attributes for size and radiometric distance representing these two groups were related to solar-elevation angle. The results suggest that detection of timber exploitation areas is limited because of the complexity of the selective-logging radiometric response. Thus, the accuracy of detecting selective logging can be influenced by the solar-elevation angle at the time of image acquisition. We conclude that images with lower solar-elevation angles are less reliable for delineation of selecting logging.

Photographic rectification of the graphic documentation of historical and archaeological heritage: The case of the southern facade of the Praetorium tower in Tarragona (Tarraco, Hispania Citerior)

This article details the use of photographic rectification as support for the graphic documentation of historical and archaeological heritage and specifically the southern facade of the Torre del Pretori (Praetorium Tower) in Tarragona. The Praetorium Tower is part of a larger monumental complex and one of the towers that connected different parts of the Tarraco Provincial Forum, the politic-administrative centre of the ancient capital of Hispania Citerioris. It is therefore a valuable example of the evolution of Roman urban architecture. The aim of this project is to provide accurate graphic documentation of the structure to facilitate the restoration and conservation of the tower, as well as to provide a more profound architectural and archaeological understanding of the Roman forum. The use of photographic rectification enabled us to overcome the spatial and time difficulties involved in collecting data caused by the size and location of the building. Specific software made it easier to obtain accurate two-dimensional images. For this reason, in our case, photographic rectification helped us to make a direct analysis of the monument and facilitated interpretation of the architectural stratigraphy. We currently separate the line of research into two concepts: the construction processes and the architecture of the building. The documentation collected permitted various analyses: the characterisation of the building modules, identification of the tools used to work the building materials, etc. In conclusion, the use of orthoimages is a powerful tool that permits the systematic study of a Roman building that has evolved over the centuries and is now in a modern urban context.

Knowledge Sharing between Generations in an Organisation - Retention of the Old or Building the New?

The study explores knowledge transfer between retiring employees and their successors in expert work. My aim is to ascertain whether there is knowledge development or building new knowledge related to this organisational knowledge transfer between generations; in other words, is the transfer of knowledge from experienced, retiring employees to their successors merely retention of the existing organisational knowledge by distributing it from one individual to another or does this transfer lead to building new and meaningful organisational knowledge. I call knowledge transfer between generations and the possibly related knowledge building in this study knowledge sharing between generations. The study examines the organisation and knowledge management from a knowledge-based and constructionist view. From this standpoint, I see knowledge transfer as an interactive process, and the exploration is based on how the people involved in this process understand and experience the phenomenon studied. The research method is organisational ethnography. I conducted the analysis of data using thematic analysis and the articulation method, which has not been used before in organisational knowledge studies. The primary empirical data consists of theme interviews with twelve employees involved in knowledge transfer in the organisation being studied and five follow-up theme interviews. Six of the interviewees are expert duty employees due to retire shortly, and six are their successors. All those participating in the follow-up interviews are successors of those soon to retire from their expert responsibilities. The organisation in the study is a medium-sized Finnish firm, which designs and manufactures electrical equipment and systems for the global market. The results of the study show that expert work-related knowledge transfer between generations can mean knowledge building which produces new, meaningful knowledge for the organisation. This knowledge is distributed in the organisation to all those that find it useful in increasing the efficiency and competitiveness of the whole organisation. The transfer and building of knowledge together create an act of knowledge sharing between generations where the building of knowledge presupposes transfer. Knowledge sharing proceeds between the expert and the novice through eight phases. During the phases of knowledge transfer the expert guides the novice to absorb the knowledge to be transferred. With the expert’s help the novice gradually comes to understand the knowledge and in the end he or she is capable of using it in his or her work. During the phases of knowledge building the expert helps the novice to further develop the knowledge being transferred so that it becomes new, useful knowledge for the organisation. After that the novice takes the built knowledge to use in his or her work. Based on the results of the study, knowledge sharing between generations takes place in interaction and ends when knowledge is taken to use. The results I obtained in the interviews by the articulation method show that knowledge sharing between generations is shaped by the novices’ conceptions of their own work goals, knowledge needs and duties. These are not only based on the official definition of the work, but also how the novices find their work or how they prioritise the given objectives and responsibilities. The study shows that the novices see their work primarily as maintenance or development. Those primarily involved in maintenance duties do not necessarily need knowledge defined as transferred between generations. Therefore, they do not necessarily transfer knowledge with their assigned experts, even though this can happen in favourable circumstances. They do not build knowledge because their view of their work goals and duties does not require the building of new knowledge. Those primarily involved in development duties, however, do need knowledge available from their assigned experts. Therefore, regardless of circumstances they transfer knowledge with their assigned experts and also build knowledge because their work goals and duties create a basis for building new knowledge. The literature on knowledge transfer between generations has focused on describing either the knowledge being transferred or the means by which it is transferred. Based on the results of this study, however, knowledge sharing between generations, that is, transfer and building is determined by how the novice considers his or her own knowledge needs and work practices. This is why studies on knowledge sharing between generations and its implementation should be based not only on the knowledge content and how it is shared, but also on the context of the work in which the novice interprets and shares knowledge. The existing literature has not considered the possibility that knowledge transfer between generations may mean building knowledge. The results of this study, however, show that this is possible. In knowledge building, the expert’s existing organisational knowledge is combined with the new knowledge that the novice brings to the organisation. In their interaction this combination of the expert’s “old” and the novice’s “new” knowledge becomes new, meaningful organisational knowledge. Previous studies show that knowledge development between the members of an organisation is the prerequisite for organisational renewal which in turn is essential for improved competitiveness. Against this background, knowledge building enables organisational renewal and thus enhances competitiveness. Hence, when knowledge transfer between generations is followed by knowledge building, the organisation kills two birds with one stone. In knowledge transfer the organisation retains the existing knowledge and thus maintains its competitiveness. In knowledge building the organisation developsnew knowledge and thus improves its competitiveness.

Model for calculating the sustainable building index (SBI) in the kingdom of Bahrain

We have developed a model that allows players in the building and construction sector and the energy policy makers on energy strategies to be able to perceive the interest of investors in the kingdom of Bahrain in conducting Building Integrated Photovoltaic (BIPV) or Building integrated wind turbines (BIWT) projects, i.e. a partial sustainable or green buildings. The model allows the calculation of the Sustainable building index (SBI), which ranges from 0.1 (lowest) to 1.0 (highest); the higher figure the more chance for launching BIPV or BIWT. This model was tested in Bahrain and the calculated SBI was found 0.47. This means that an extensive effort must be made through policies on renewable energy, renewable energy education, and incentives to BIPV and BIWT projects, environmental awareness and promotion to clean and sustainable energy for building and construction projects. Our model can be used internationally to create a "Global SBI" database. The Sustainable building and construction initiative (SBCI), United Nation, can take the task for establishing such task using this model.

Reliability in the whole life cycle of building systems

Purpose – The purpose of this research is to show that reliability analysis and its implementation will lead to an improved whole life performance of the building systems, and hence their life cycle costs (LCC). Design/methodology/approach – This paper analyses reliability impacts on the whole life cycle of building systems, and reviews the up-to-date approaches adopted in UK construction, based on questionnaires designed to investigate the use of reliability within the industry. Findings – Approaches to reliability design and maintainability design have been introduced from the operating environment level, system structural level and component level, and a scheduled maintenance logic tree is modified based on the model developed by Pride. Different stages of the whole life cycle of building services systems, reliability-associated factors should be considered to ensure the system's whole life performance. It is suggested that data analysis should be applied in reliability design, maintainability design, and maintenance policy development. Originality/value – The paper presents important factors in different stages of the whole life cycle of the systems, and reliability and maintainability design approaches which can be helpful for building services system designers. The survey from the questionnaires provides the designers with understanding of key impacting factors.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.