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 particular, we use two scales originating what we call contextual and local edges, obtained with filters of, respectively, large and small footprints. Contextual edges are robust to noise and our method uses them validate local edges, i.e., to only select the local edges that correspond to the same intensity transition (dark-to-bright or vice-versa). Line segment connectivity is enforced by joining the valid local edges whose distance does not exceed a threshold. To enable dealing with situations where the edges divide regions of non-uniform intensity distributions, e.g., textures, the contextual edges are decided by using a two-sample statistical test. We present experiments that illustrate how our method is efficient in extracting complete segments in several situations where current methods fail.

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 situations that occur in many realistic scenarios, e.g., when line segments cross or when long segments are corrupted. We address the critical limitations of the HT as a line segment extractor by incorporating connectivity in the voting process. This is done by only accounting for the contributions of edge points lying in increasingly larger neighborhoods and whose position and directional information agree with potential line segments. As a result, our method, which we call segment extraction by connectivity-enforcing HT (STRAIGHT), extracts the longest connected segments in each location of the image, thus also integrating into the HT voting process the usually separate step of individual segment extraction. The usage of the Hough space mapping and a corresponding hierarchical implementation make our approach computationally feasible. We present experiments that illustrate, with synthetic and real images, how STRAIGHT succeeds in extracting complete segments in situations where current methods fail.

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 (interrupted lines) and typically fail when line segments cross. We propose a new method that tackles these problems: first, rough estimates of plural candidate directions at each edge point are obtained through a directional local HT; then, the parameters determining the line segments are globally estimated by maximizing a quality measure that depends on all the edge points. Our experiments illustrate that the proposed method outperforms current methods in challenging situations.

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. Although others have proposed learning-based methods, we are the first to simultaneously achieve discrimination rates comparable with state-of-the art methods at high frame rates. We particularize the general methodology to different filter structures, e.g., rotationally discriminant filters and rotationally invariant ones. We use Genetic Algorithms for learning and test our method against state-of-the-art ones, using the Brodatz album.

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 occlusion and limited field of view. The paper describes a new method to recover 3D structures from videos with partially overlapping views. The well known factorization method recovers 3D rigid structures by factoring an observation matrix that collects the trajectories of feature points (see Tomasi, C. and Kanade, T., Int. J. of Computer Vision, vol.9, no.2, 1992). We extend this method to the more challenging scenario of observing incomplete trajectories. In this way, we accommodate not only features that disappear, but also features that, although not visible in the first image, become available later. Under this scenario, the observation matrix has missing entries. We develop three new algorithms to factor out matrices with missing data. Experiments with synthetic data and real video images demonstrate the viability of our approach to recover 3D structure.