Jorge Badenas

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This paper presents new algorithms to identify and eliminate mislabelled, noisy and atypical training samples for supervised learning and more specifically, for nearest neighbour classification. The main goal of these approaches is to enhance the classification accuracy by improving the quality of the training data. Several experiments with synthetic and(More)
This paper is concerned with an eecient estimation and seg-mentation of 2-D motion from image sequences, with the focus on traac monitoring applications. In order to reduce the computational load and facilitate real-time implementation, the proposed approach m a k es use of simplifying assumptions that the camera is stationary and that the projection of(More)
This paper 1 describes a method for tracking regions in image sequences. Regions segmented from each frame by a motion segmentation technique are matched by using a relaxation procedure. Matching is based on measuring the similarity of the regions from the current frame and a list of regions corresponding to objects. A Kalman filter is used in order to(More)
Image registration is a problem that arises in many image processing applications whenever information from two or more scenes have to be aligned. In image registration the use of an adequate measure of alignment is a crucial issue. Current techniques are classified in two broad categories: area based and feature based. All methods include some similarity(More)
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