Learning Descriptors for Object Recognition and 3D Pose Estimation

Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.Comment: CVPR 201

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Do Foreign Exchange Markets Matter Dor Industry Stock Returns ? An empirical investigation

In this paper, we develop a bivariate two factor-two country GARCH model of stock returns in order to investigate whether exchange rate fluctuations have a significant impact on the conditional mean, variance, and correlation of industry stock returns. Weekly data for seven industries in five European countries over the 1990-1998 period are used. We document that exchange rates have a significant effect on expected industry stock returns and on their volatility. The magnitude of this effect is, however, quite small. The contribution of the exchange rate factor to the time-varying correlation coefficients between two countries’industry returns is also very modest. The paper also shows that the importance of the exchange rate spillovers is influenced by the exchange rate regime, the magnitude and the direction of exchange rate shocks.Industry stock returns; Fx market; Volatility; International correlation

“Sick with worry...” Stories from the front-line of inequality, 2015

Contains some sobering stories of poverty and inequality in prosperous Australia, finding overwhelmingly that people do not experience poverty because they choose to but as a result of a range of structural causes which push them to the margins. Executive summary In late 2013, the St Vincent de Paul Society published research outlining some startling statistics about poverty in Australia today. Two Australias: A report on poverty in the land of plenty showed that 13 per cent of the population is living in poverty, 1.5 million people are unemployed or underemployed, the bottom fifth of households receive only 2.5 per cent of wages, and a quarter of us live with a long-term health condition or disability. The report concluded that, under Australia’s prosperous veneer, there is a significant group of people who are struggling just to survive.   Behind the numbers are the faces. Following from that quantitative survey, the St Vincent de Paul Society decided to conduct the present research because we wanted to hear the stories of those doing it toughest. Those for whom every day is a battle. Stories from the other Australia.   We sent out a call to our members and volunteers, and over 70 interviews were conducted around the country. When we read the stories, some key themes emerged. These themes will not be a surprise to anyone who is familiar with disadvantage in Australia today: there is a severe shortage of stable, affordable housing; incomes for many are not sufficient for a decent standard of living, and secure work is very hard to find; and Australians living with disability continue to face severe structural barriers to participation. Cutting across all three areas were several further issues: the stigma faced by those on the edge; the inherent insecurity that life entails for many in the other Australia; and the disproportionate impact of poverty on women.   However, what also shone through our research were three remarkable opportunities for change. First, supportive, rights-based services can and do help many people out of poverty. Secondly, people’s overwhelming love for their children presents a wonderful lens through which to see change happen. And, finally, what almost everyone desires above all else is to be able to participate.   Therefore, while it seems there are structural problems around housing, employment and disability that are systemically excluding people, the research shows that the way forward involves better service provision and harnessing people’s keen desire to contribute

Biotechnological modification and functionalisation of polyester surfaces

Synthetic fibers form an important part of the textile industry, the production of polyester alone surpassing that of cotton. A disadvantage of synthetic fibers is their low hydrophilicity. Polyester fibers are particularly hydrophobic. This affects the processability and functionalisation of the fibers. A relatively new and promising alternative is the use of enzymes in surface modification of synthetic fibers. Synthetic materials have generally been considered resistant to biological degradation; recent developments at different research groups demonstrate that enzymes are very well capable of hydrolysing synthetic materials