Rui F. C. Guerreiro

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Several computer vision applications require estimating a rank deficient matrix from noisy observations of its entries. When the observation matrix has no missing data, the LS solution of such problem is known to be given by the SVD. However, in practice, when several entries of the matrix are not observed, the problem has no closed form solution. In this(More)
The majority of methods available to recover 3D structure from video assume that a set of feature points are tracked across a large number of frames. This is not always possible in real videos because the images overlap only partially, due to the occlusion and the limited field of view. This paper describes a new method to recover 3D structure from videos(More)
The approaches to global motion estimation have been naturally classified into one of two main classes: feature-based methods and direct (or featureless) methods. Feature-based methods compute a set of point correspondences between the images and, from these, estimate the parameters describing the global motion. Although the simplicity of the second step(More)
Global voting schemes based on the Hough transform (HT) have been widely used to robustly detect lines in images. However, since the votes do not take line connectivity into account, these methods do not deal well with cluttered images. On the other hand, the so-called local methods enforce connectivity but lack robustness to deal with challenging(More)
Matrix factorization methods are now widely used to recover 3D structure from 2D projections [1]. In practice, the observation matrix to be factored out has missing data, due to the limited field of view and the occlusion that occur in real video sequences. In opposition to the optimality of the SVD to factor out matrices without missing entries, the(More)
Current methods for line segment extraction often fail in challenging scenarios that abound in real-life images, e.g., those containing corrupted lines, of various widths, with multiple crossings, and immersed in clutter. We propose a method that tackles these issues by combining multiscale edges while taking line segment connectivity into account. In(More)
Although global voting schemes, such as the Hough Transform (HT), have been widely used to robustly detect lines in images, they fail when the line segments at hand are short, particularly if the underlying edge maps are cluttered. Line segment detection in these scenarios has been addressed using local methods, which lack robustness to missing data(More)
Current texture analysis methods enable good discrimination but are computationally too expensive for applications which require high frame rates. This occurs because they use redundant calculations, failing in capturing the essence of the texture discrimination problem. In this paper we use a learning approach to obtain simple filters for this task.(More)
When performing texture analysis via standard filter banks, good discrimination depends on the usage of a large number of filters. For example, when using the popular Gabor Filter Banks, the typical number of filters ranges from about ten to fifty. For applications requiring high frame rate processing, this is too complex. Also, discrimination may be poor(More)
The Discrete Cosine Transform (DCT) is widely used in lossy image and video compression schemes, e.g., JPEG and MPEG. In this paper, we show that the compression efficiency of the DCT is dependent on the edge directions within a block. In particular, higher compression ratios are achieved when edges are aligned with the image axes. To maximize compression(More)