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Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each...
COACH (COrrective Advice Communicated by Humans) is an interactive learning framework that allows non-expert humans to shape a policy through corrective advice, using a binary signal in the action domain of the agent. The original COACH formulation has been tested in problems of one-dimensional actions spaces with RBF linear models for the policy approximation. In this paper the COACH framework is...
Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based...
Recent research in piecewise linear regression for Super-Resolution has shown the positive impact of training regressors with densely populated clusters whose datapoints are tight in the Euclidean space. In this paper we further research how to improve the locality condition during the training of regressors and how to better select them during testing time. We study the characteristics of the metrics...
This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques...
Circular data is very relevant in many fields such as Geostatistics, Mobile Robotics and Pose Estimation. However, some existing angular regression methods do not cope with arbitrary nonlinear functions properly. Moreover, some other regression methods that do cope with nonlinear functions, like Gaussian Processes, are not designed to work well with angular responses. This paper presents two novel...
Building robust and fast object detection systems is an important goal of computer vision. A problem arises when several object types are to be detected, because the computational burden of running several specific classifiers in parallel becomes a problem. In addition the accuracy and the training time can be greatly affected. Seeking to provide a solution to these problems, we extend cascade classifiers...
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