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In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carrie ...
In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant t ...
This book documents the discussions that came from the seminar between six leading Swiss and American Schools including ETH, CCA and University of California ...
California College of the Arts / University of California Berkeley / William Stout2006
This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with ...
We define a new task of question answering on meeting records and assess its difficulty in terms of types of information and retrieval techniques required. The importance of this task is revealed by the increasingly growing interest in the design of sophis ...
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...
This paper presents an effective implementation of detection-localization of multiple speech sources with microphone arrays. In particular, the Scaled Conjugate Gradient descent is used for fast and precise localization, within a pre-detected volume of spa ...
Dynamic Magnetic Resonance Imaging (MRI) with contrast media injection is an important tool to study renal perfusion in humans and animals. The goal of this study is to build classifiers for the automatic classification of a kidney as healthy or pathologic ...
Text categorization is intrinsically a supervised learning task, which aims at relating a given text document to one or more predefined categories. Unfortunately, labeling such databases of documents is a painful task. We present in this paper a method tha ...
The work presented in this thesis deals with several problems met in information retrieval (IR), task which one can summarise as identifying, in a collection of "documents", a subset of documents carrying a sought information, i.e.. relevant for a request ...