Robust and Efficient 3D Recognition by Alignment

Alignment is a prevalent approach for recognizing 3D objects in 2D images. A major problem with current implementations is how to robustly handle errors that propagate from uncertainties in the locations of image features. This thesis gives a technique for bounding these errors. The technique makes use of a new solution to the problem of recovering 3D pose from three matching point pairs under weak-perspective projection. Furthermore, the error bounds are used to demonstrate that using line segments for features instead of points significantly reduces the false positive rate, to the extent that alignment can remain reliable even in cluttered scenes.

Anthropometry of the Breast Region: How to Measure?

Breast region measurements are important for research, but they may also become significant in the legal field as a quantitative tool for preoperative and postoperative evaluation. Direct anthropometric measurements can be taken in clinical practice. the aim of this study was to compare direct breast anthropometric measurements taken with a tape measure and a compass.Forty women, aged 18-60 years, were evaluated. They had 14 anatomical landmarks marked on the breast region and arms. the union of these points formed eight linear segments and one angle for each side of the body. the volunteers were evaluated by direct anthropometry in a standardized way, using a tape measure and a compass.Differences were found between the tape measure and the compass measurements for all segments analyzed (p > 0.05).Measurements obtained by tape measure and compass are not identical. Therefore, once the measurement tool is chosen, it should be used for the pre- and postoperative measurements in a standardized way.This journal requires that authors assign a level of evidence to each article. for a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Il Sistema di Revenue Management strumento per lo sviluppo turistico sostenibile delle destinazioni

Businesses interact constantly with the environment, realizing several and heterogeneous exchanges. Organizations can be considered a system of different interests, frequently conflicting and the satisfaction of different stakeholders is a condition of success and survival. National and international literature attempts to explain the complex connection between companies and environment. In particular, the Stakeholder Theory considers crucial for businesses the identification of different stakeholders and their involvement in decision-making process. In this context, profit can not be considered the only purpose of companies existence and business aims become more numerous and different. The Stakeholder Theory is often utilized as framework for tourism studies, in particular in Sustainable Tourism Development research. In fact, authors consider sustainable the tourism development able to satisfy interests of different stakeholders, traditionally identified as local community and government, businesses, tourists and natural environment. Tourism businesses have to guarantee the optimal use of natural resources, the respect of socio-cultural tradition of local community and the creation of socio-economic benefits for all stakeholders in destinations. An obstacle to sustainable tourism development that characterizes a number of destinations worldwide is tourism demand seasonality. In fact, its negative impact on the environment, economy and communities may be highly significant. Pollution, difficulties in the use of public services, stress for residents, seasonal incomes, are all examples of the negative effects of seasonality. According to the World Tourism Organization (2004) the limitation of seasonality can favour the sustainability of tourism. Literature suggests private and public strategies to minimize the negative effects of tourism seasonality, as diversification of tourism products, identification of new market segments, launching events, application of public instruments like eco-taxes and use of differential pricing policies. Revenue Management is a managerial system based on differential pricing and able to affect price sensitive tourists. This research attempts to verify if Revenue Management, created to maximize profits in tourism companies, can also mitigate the seasonality of tourism demand, producing benefits for different stakeholders of destinations and contributing to Sustainable Tourism Development. In particular, the study attempts to answer the following research questions: 1) Can Revenue Management control the flow of tourist demand? 2) Can Revenue Management limit seasonality, producing benefits for different stakeholders of a destination? 3) Can Revenue Management favor the development of Sustainable Tourism? The literature review on Stakeholder Theory, Sustainable Tourism Development, tourism seasonality and Revenue Management forms the foundation of the research, based on a case study approach looking at a significant destination located in the Southern coast of Sardinia, Italy. A deductive methodology was applied and qualitative and quantitative methods were utilized. This study shows that Revenue Management has the potential to limit tourism seasonality, to mitigate negative impacts occurring from tourism activities, producing benefits for local community and to contribute to Sustainable Tourism Development.

Visual Segmentation-Based Data Record Extraction From Web Documents

Li, Longzhuang, Liu, Yonghuai, Obregon, A., Weatherston, M. Visual Segmentation-Based Data Record Extraction From Web Documents. Proceedings of IEEE International Conference on Information Reuse and Integration, 2007, pp. 502-507. Sponsorship: IEEE

Construction of a Festuca pratensis BAC library for map-based cloning in IFestulolium substitution lines

Iain S. Donnison, Donal M. O Sullivan, Ann Thomas, Peter Canter, Beverley Moore, Ian Armstead, Howard Thomas, Keith J. Edwards and Ian P. King (2005). Construction of a Festuca pratensis BAC library for map-based cloning in Festulolium substitution lines. Theoretical and Applied Genetics, 110 (5) pp.846-851 Sponsorship: BBSRC;BBSRC RAE2008

Molecular analysis of the anaerobic rumen fungus Orpinomyces - insights into an AT-rich genome

Matthew J. Nicholson, Michael K. Theodorou and Jayne L. Brookman. (2005). Molecular analysis of the anaerobic rumen fungus Orpinomyces - insights into an AT-rich genome. Microbiology, 151 (1), 121-133. Sponsorship: BBSRC RAE2008

O interdyscyplinarności komparatystyki

For the past fifty years, the interest in issues beyond pure philology has been a watchword in comparative literary studies. Comparative studies, which by default employ a variety of methods, run the major risk – as the experience of American comparative literature shows – of descending into dangerous ‘everythingism’ or losing its identity. However, it performs well when literature remains one of the segments of comparison. In such instances, it proves efficacious in exploring the ‘correspondences of arts’, the problems of identity and multiculturalism as well as contributes to the research into the transfer of ideas. Hence, it delves into phenomena which exist on the borderlines of literature, fine arts and other fields of humanities, employing strategies of interpretation which are typical for each of those fields. This means that in the process there emerges a “borderline methodology”, whose distinctive feature is heterogeneity of conducting research. This, in turn, requires the scholar to be both ingenious and creative while selecting topics as well as to possess competence in literary studies and the related field.

Análise das atitudes e comportamentos dos consumidores face à compra de produtos alimentares de marca do distribuidor

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências da Comunicação, especialização em Marketing e Publicidade

A imagem de um destino turístico cultural

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Empresariais.

Women in the Legal Profession

In recent years, the high percentage of lawyers in Portugal became a controversial issue. As a large number of law graduates have been competing for admission at the Bar, this trend is creating new challenges to the profession, with important resonances in the Bar admission policy. The purpose of this presentation is to illustrate the progress made by women in legal professions, in Portugal, over the last decades. In order to contextualize our analysis, we begin with an overview of the position of women in the labor market and then focus on the legal professions. Firstly, the increasing presence of women in different segments of the legal field is analyzed by means of a statistical approach. Afterwards, we draw a critical analysis highlighting the bearing of these developments and deconstructing their meaning in terms of career patterns, remuneration and professional status. Our analysis of contemporary official data on legal professions reveals that even though women are occupying a growing number of positions in private practice, they earn lower salaries, have lower job satisfaction and have a more critical reasoning towards the public image of lawyers. Concerning magistrates, women working in superior courts continue to be underrepresented. Overall, we conclude that the increasing integration of women in legal professions is not straightforward, and there are still many aspects that need to be addressed the private and public sector.

O processo de consumo de e-books

Dissertação apresentada à Universidade Fernando Pessoa como parte dos requisitos para a obtenção do grau de Mestre em Ciências da Comunicação, ramo de Marketing e Publicidade

Color Region Grouping and Shape Recognition with Deformable Models

A new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.

Generalized Methods for Discovering Frequent Poly-Regions in DNA

The problem of discovering frequent poly-regions (i.e. regions of high occurrence of a set of items or patterns of a given alphabet) in a sequence is studied, and three efficient approaches are proposed to solve it. The first one is entropy-based and applies a recursive segmentation technique that produces a set of candidate segments which may potentially lead to a poly-region. The key idea of the second approach is the use of a set of sliding windows over the sequence. Each sliding window covers a sequence segment and keeps a set of statistics that mainly include the number of occurrences of each item or pattern in that segment. Combining these statistics efficiently yields the complete set of poly-regions in the given sequence. The third approach applies a technique based on the majority vote, achieving linear running time with a minimal number of false negatives. After identifying the poly-regions, the sequence is converted to a sequence of labeled intervals (each one corresponding to a poly-region). An efficient algorithm for mining frequent arrangements of intervals is applied to the converted sequence to discover frequently occurring arrangements of poly-regions in different parts of DNA, including coding regions. The proposed algorithms are tested on various DNA sequences producing results of significant biological meaning.

Deformable Shape Detection and Description via Model-Based Region Grouping

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color, or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.

An Invariant Representation for Matching Trajectories across Uncalibrated Video Streams

We introduce a view-point invariant representation of moving object trajectories that can be used in video database applications. It is assumed that trajectories lie on a surface that can be locally approximated with a plane. Raw trajectory data is first locally approximated with a cubic spline via least squares fitting. For each sampled point of the obtained curve, a projective invariant feature is computed using a small number of points in its neighborhood. The resulting sequence of invariant features computed along the entire trajectory forms the view invariant descriptor of the trajectory itself. Time parametrization has been exploited to compute cross ratios without ambiguity due to point ordering. Similarity between descriptors of different trajectories is measured with a distance that takes into account the statistical properties of the cross ratio, and its symmetry with respect to the point at infinity. In experiments, an overall correct classification rate of about 95% has been obtained on a dataset of 58 trajectories of players in soccer video, and an overall correct classification rate of about 80% has been obtained on matching partial segments of trajectories collected from two overlapping views of outdoor scenes with moving people and cars.