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In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing the real labels. For each sample, labels (which may not be the same) are provided by different annotators (usually with different degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios where each annotator may label a subset of the training set samples only...
Active object detection refers to the problem of determining the existence and location of objects in an image by actively selecting which regions of the image to explore. Herein, an object detection algorithm is proposed that models image regions as vertices and overlap relationships as edges in a directed weighted graph. Information is propagated from labeled vertices through graph edges that operate...
State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a de-noising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-based using Gaussian mixture models) in a so-called...
Speech Intelligibility Prediction (SIP) algorithms are becoming increasingly popular for objective evaluation of speech processing algorithms and transmission systems. Most often, SIP algorithms aim to predict the average intelligibility of an average listener in some specific listening condition. In the present work, we instead consider the aim of predicting the intelligibility of single words. I...
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