Line extraction is a preliminary step in various visual robotic tasks performed in low textured scenes such as city and indoor settings. Several efficient line segment detection algorithms such as LSD and EDLines have recently emerged. However, the state of the art segment grouping methods are not robust enough or not amenable for detecting lines in real-time. In this paper we present FSG, a fast and robust line detection algorithm. It is based on two independent components. A proposer that greedily cluster segments suggesting plausible line candidates and a probabilistic model that decides if a group of segments is an actual line. In the experiments we show that our procedure is more robust and faster than the best methods in the literature and achieves state-of-the art performance in a high level robot localization task such as vanishing points detection.

A fundamental step to solve bin-picking and grasping problems is the accurate estimation of an object 3D pose. Such visual task usually rely on profusely textured objects: standard procedures such as detection of interest points or computation of appearance-based descriptors are favoured by using a highly informative surface. However, texture-less objects or their parts (i.e., those whose surface texture is poorly conditioned) are common in any environment but still challenging to deal with. This is due the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. In this paper, we propose a method to estimate the 3D pose for texture-less objects given a coarse initialization: the pose is estimated using using edge correspondences, where the similarity measure is encoded using a pre-computed linear regression matrix. Furthermore, we also propose a method to increase the robustness of the estimated pose against background and object clutter. We validate both methods by using synthetic and real image sequences with objects with known ground truth.

This paper presents a method for 6D pose estimation from a single RGB image for complex texture-less objects. This class of objects are common in any environment but still challenging to deal with. This is due to the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. Here we propose a novel part-based method using an efficient template matching approach where each template independently encodes the similarity function using a Forest trained over the templates. Moreover, accuracy is even more incremented by using a cascade of the learned forest. These templates forests together with the simplicity of the computed image features allow a quick estimate of the pose achieving real-time performance. Performance are demonstrated both on synthetic and real images with known ground truth.

Fuzzy queueing theory (FQT) is a powerful tool used to model queueing systems taking into account their natural imprecision. The traditional FQT model assumes a fixed number of servers with constant fuzzy arrival and service rates. It is quite common, however, to encounter situations where the number of servers, the arrival rate, and the service rate change with time. In those cases, the variability of these parameters can significantly impact the behavior of the queueing system and complicate its analysis. In this paper, we propose a simulation method to deal with these problems that leads to realistic estimations of the behavior of the queueing system. To demonstrate the applicability of the technique, we have employed it to solve a real-world problem: the prediction of the impact of electric vehicles on power networks. Electric vehicles have recently become a popular topic for research and development, mainly for their potential ability to reduce emissions and dependence on fossil fuels. Their demanding charging requirements can, nevertheless, significantly impact the performance of the power grid. In this paper, we propose modeling the recharging process as a fuzzy queueing problem. This approach is more realistic than previous treatments, while leading to accurate descriptions of the performance of the system.

Electric vehicles are a promising technology for reducing CO2emissions and dependence on fossil fuels. Their market is expected to grow quickly in the next years with the increased concern about global warming and the advances in technology. However, due to their large batteries, with high energy capacity, their impact on power networks remains uncertain. Several studies have been carried out trying to predict this impact. In this paper we propose a more realistic approach based on modeling electric-vehicle recharging as a queueing problem. Vehicles arrive following a nonhomogeneous Poisson process with fuzzy arrival rate and their energy requirements follow an exponential distribution. To solve this queueing problem we will use a simulation procedure that will provide realistic quality measures about the performance of the system. Additionally, we present a case study on the impacts of electric vehicles on the Spanish power network. In particular we will analyze two possible scenarios corresponding to the expected situation for years 2014 and 2020.

We present an efficient algorithm for fitting a morphable model to an image sequence. It is built on a projective geometry formulation of perspective projection, which results in a linear mapping from 3D shape to the projective plane, and a factorisation of this mapping into matrices that can be partially computed off-line. This algorithm can cope with full 360 degrees object rotation and linear deformations. We validate our approach using synthetically generated and real sequences. Compared to a plain Lucas-Kanade implementation, we achieve a six fold increase in performance for a rigid object and two fold for a non-rigid face.

Pixel brightness variations in an image sequence depend both on the objects ‘surface reflectance and on the motion of the camera and object. In the case of rigid shapes some proposed models have been very successful explaining the relation among these strongly coupled components. On the other hand, shapes which deform pose new challenges since the relation between pixel brightness variation with non-rigid motion is not yet clear. In this paper, we introduce a new model which describes brightness variations with two independent components represented as linear basis shapes. Lighting influence is represented in terms of Spherical Harmonics and non-rigid motion as a linear model which represents image coordinates displacement. We then propose an efficient procedure for the estimation of this image model in two distinct steps. First, shape normal’s and albedo are estimated using standard photometric stereo on a sequence with varying lighting and no deformable motion. Then, given the knowledge of the object’s shape normal’s and albedo, we efficiently compute the 2D coordinates bases by minimizing image pixel residuals over an image sequence with constant lighting and only non-rigid motion. Experiments on real tests show the effectiveness of our approach in a face modelling context.

We introduce a multi-view direct procedure for efficiently tracking 3D objects. It is an extension of Hager and Belhumeur’s factorisation approach to the case of three-dimensional objects and multi-camera setup. By tracking a 3-D object we mean to estimate the pose and location of the object through a video sequence. A novel parameterisation of the object texture allow us to compute the Jacobian that emerges in the minimisation in a way it has a large constant part. The pixels viewed by each camera determine the rows of the Jacobian used for tracking. We perform qualitative and quantitative experiments confirming the validity of the approach.

We introduce a subspace representation of face appearance which separates facial expressions from illumination variations. The appearance of a face is represented by the addition of two approximately independent linear subspaces modelling facial expressions and illumination respectively. The independence assumption notably simplifies the training of the system. We only require two image sequences. One in which one facial expression is subject to all possible illuminations and another in which the face, under one illumination, performs all facial expressions. This simple model enables us to train the system with no manual intervention. We also introduce an efficient procedure for fitting this model, which can be used for tracking a human face in real-time. 1

Efficient incremental image alignment is a topic of renewed interest in the computer vision community because of its applications in model fitting and model-based object tracking. Successful compositional procedures for aligning 2D and 3D models under weak-perspective imaging conditions have already been proposed. Here we present a mixed compositional and additive algorithm which is applicable to the full projective camera case.