Nonparametric Bayesian methods in robotic vision

In this dissertation non-parametric Bayesian methods are used in the application of robotic vision. Robots make use of depth sensors that represent their environment using point clouds. Non-parametric Bayesian methods can (1) determine how good an object is recognized, and (2) determine how many objects a particular scene contains. When there is a model available for the object to be recognized and the nature of perceptual error is known, a Bayesian method will act optimally.In this dissertation Bayesian models are developed to represent geometric objects such as lines and line segments (consisting out of points). The infinite line model and the infinite line segment model use a non-parametric Bayesian model, to be precise, a Dirichlet process, to represent the number of objects. The line or the line segment is represented by a probability distribution. The lines can be represented by conjugate distributions and then Gibbs sampling can be used. The line segments are not represented by conjugate distributions and therefore a split-merge sampler is used.A split-merge sampler fits line segments by assigning points to a hypothetical line segment. Then it proposes splits of a single line segment or merges of two line segments. A new sampler, the triadic split-merge sampler, introduces steps that involve three line segments. In this dissertation, the new sampler is compared to a conventional split-merge sampler. The triadic sampler can be applied to other problems as well, i.e., not only problems in robotic perception.The models for objects can also be learned. In the dissertation this is done for more complex objects, such as cubes, built up out of hundreds of points. An auto-encoder then learns to generate a representative object given the data. The auto-encoder uses a newly defined reconstruction distance, called the partitioning earth mover’s distance. The object that is learned by the auto-encoder is used in a triadic sampler to (1) identify the point cloud objects and to (2) establish multiple occurrences of those objects in the point cloud.Algorithms and the Foundations of Software technolog

The impact of the International Livestock Research Institute

Providing the first evidence-based global estimates of the many scientific, economic, policy, and capacity development impacts of livestock research in and for developing countries, this volume is an indispensable guide and reference for veterinarians, animal and forage scientists, and anyone working for the equitable and sustainable development of the world's poorer agricultural economies. Livestock is one of the fastest growing agricultural sectors, with most growth occurring in developing countries. For more than four and a half decades one global centre has been mandated to conduct research on leveraging the benefits and mitigating the costs of livestock production in poor countries. This book focuses on the achievements, failures and impacts of the International Livestock Research Institute (ILRI) and its predecessors, the International Livestock Centre for Africa (ILCA) and the International Laboratory for Research on Animal Diseases (ILRAD). The scientific and economic impacts of tropical livestock research detailed in this work reveal valuable lessons for reducing world hunger, poverty and environmental degradation. Describing the impacts of smallholder livestock systems on the global environment, the book also covers animal genetics, production, health and disease control, and livestock-related land management, public policy and economics, all with useful pointers for future livestock-for-development research

Actas de las XXXIV Jornadas de Automática

Postprint (published version