Juan José Pantrigo

Variable neighborhood search for the Vertex Separation Problem

The Vertex Separation Problem belongs to a family of optimization problems in which the objective is to find the best separator of vertices or edges in a generic graph. This optimization problem is strongly related to other well-known graph problems; such as the Path-Width, the Node Search Number or the Interval Thickness, among others. All of these optimization problems are NP-hard and have practical applications in VLSI (Very Large Scale Integration), computer language compiler design or graph drawing. Up to know, they have been generally tackled with exact approaches, presenting polynomial-time algorithms to obtain the optimal solution for specific types of graphs. However, in spite of their practical applications, these problems have been ignored from a heuristic perspective, as far as we know. In this paper we propose a pure 0-1 optimization model and a metaheuristic algorithm based on the variable neighborhood search methodology for the Vertex Separation Problem on general graphs. Computational results show that small instances can be optimally solved with this optimization model and the proposed metaheuristic is able to find high-quality solutions with a moderate computing time for large-scale instances.

Differential optical flow applied to automatic facial expression recognition

This work compares systematically two optical flow-based facial expression recognition methods. The first one is featural and selects a reduced set of highly discriminant facial points while the second one is holistic and uses much more points that are uniformly distributed on the central face region. Both approaches are referred as feature point tracking and holistic face dense flow tracking, respectively. They compute the displacements of different sets of points along the sequence of frames describing each facial expression (i.e. from neutral to apex). First, we evaluate our algorithms on the Cohn-Kanade database for the six prototypic expressions under two different spatial frame resolutions (original and 40%-reduced). Later, our methods were also tested on the MMI database which presents higher variabilities than the Cohn-Kanade one. The results on the first database show that dense flow tracking method at original resolution slightly outperformed, in average, the recognition rates of feature point tracking method (95.45% against 92.42%) but it requires 68.24% more time to track the points. For the patterns of MMI database, using dense flow tracking at the original resolution, we achieved very similar average success rates.

Particle filter on GPUs for real-time tracking

Efficient object tracking is required by many Computer Vision application areas like surveillance or robotics. It deals with statespace variables estimation of interesting features in image sequences and their future prediction. Probabilistic algorithms has been widely applied to tracking. These methods take advantage of knowledge about previous states of the system reducing the computational cost of an exhaustive search over the whole image. In this framework, posterior probability density function (pdf) of the state is estimated in two stages: prediction and update. General particle filters are based on discrete representations of probability densities and can be applied to any state-space model [Arulampalam et al. 2002]. Discrete particles j of a set (Xt ,Πt) = {(x0 t ,π0 t )...(xN t ,πN t )} in time step t, contains information about one possible state of the system x j t and its importance weight π j t . In a practical approach, particle weights computation is the most expensive stage of the particle filter algorithm, and it has to be executed at each time step for every particle [Deutscher et al. 2000].

Variable Formulation Search for the Cutwidth Minimization Problem

Many optimization problems are formulated as min-max problems where the objective function consist of minimizing a maximum value. In this case, it is usual that many solutions of the problem has associated the same value of the objective function. When this happens it is difficult to determine which solution is more promising to continue the search. In this paper we propose a new variant of the Variable Neighbourhood Search methodology to tackle this kind of problems. The new variant, named Variable Formulation Search, makes use of alternative formulations of the problem to determine which solution is more promising when they have the same value of the objective function in the original formulation. We do that in shaking, local search and neighbourhood change steps of the basic Variable Neighbourhood Search. We apply the new methodology to the Cutwidth Minimization Problem. Computational results show that our proposal outperforms previous algorithms in the state of the art in terms of quality and computing time.

Linguistic description of traffic in a roundabout

The linguistic description of a physical phenomenon is a summary of the available information where certain relevant aspects are remarked while other irrelevant aspects remain hidden. This paper deals with the development of computational systems capable to generate linguistic descriptions from images captured by a video camera. The problem of linguistically labeling images in a database is a challenge where still much work remains to be done. In this paper, we contribute to this field using a model of the observed phenomenon that allows us to interpret the content of images. We build the model by combining techniques from Computer Vision with ideas from the Zadehs Computational Theory of Perceptions. We include a practical application consisting of a computational system capable to provide a linguistic description of the behavior of traffic in a roundabout.

Accurate three-dimensional pose recognition from monocular images using template matched filtering

Abstract. An accurate algorithm for three-dimensional (3-D) pose recognition of a rigid object is presented. The algorithm is based on adaptive template matched filtering and local search optimization. When a scene image is captured, a bank of correlation filters is constructed to find the best correspondence between the current view of the target in the scene and a target image synthesized by means of computer graphics. The synthetic image is created using a known 3-D model of the target and an iterative procedure based on local search. Computer simulation results obtained with the proposed algorithm in synthetic and real-life scenes are presented and discussed in terms of accuracy of pose recognition in the presence of noise, cluttered background, and occlusion. Experimental results show that our proposal presents high accuracy for 3-D pose estimation using monocular images.

Combining intensification and diversification strategies in VNS. An application to the Vertex Separation problem

The Vertex Separation problem (VSP) is an NP-hard problem with practical applications in VLSI design, graph drawing and computer language compiler design. VSP belongs to a family of optimization problems in which the objective is to find the best separator of vertices or edges in a generic graph. In this paper, we propose different heuristic methods and embed them into a Variable Neighborhood Search scheme to solve this problem. More precisely, we propose (i) a constructive algorithm, (ii) four shake procedures, (iii) two neighborhood structures, (iv) efficient algorithmic strategies to explore them, (v) an extended version of the objective function to facilitate the search process and finally, (vi) we embed these strategies in a Reduced Variable Neighborhood Search (RVNS), a Variable Neighborhood Descent (VND) and a General Variable Neighborhood Search (GVNS). Additionally, we provide an extensive experimental comparison among them and with the best previous method of the literature. We consider three different benchmarks, totalizing 162 representative instances. The experimentation reveals that our best procedure (GVNS) improves the state of the art in both quality and computing time. This fact is confirmed by non-parametric statistical tests. In addition, when considering only the largest instances, the other two proposed variants (RVNS and VND) also obtain statistically significant differences with respect to the best previous method identified in the state of the art.

Multiple and variable target visual tracking for video-surveillance applications

Visual detection and tracking are interdisciplinary tasks which are oriented at estimating the state of one or multiple moving objects in a video sequence. This is one of the first tasks in processing video systems which try to describe human behaviour in different contexts, such as video-surveillance, sport technique analysis. This work presents a multiple object tracking system which properly hybridizes particle filters and memetic algorithms to produce a more reliable and efficient tracking algorithm. The system has been tested on synthetic and real image sequences, with the aim of describing their performance for different levels of noise, occlusions, a variable number of objects, etc. Experimental results demonstrate that the proposed system accurately tracks multiple objects in the scene, by grouping and ungrouping them when necessary, while keeping their identities during the sequence of images. Moreover, the performance of the proposed system is not strongly affected by the increase in the number of objects, maintaining computational load and precision in proper balance.

Branch and bound for the cutwidth minimization problem

The cutwidth minimization problem consists of finding a linear arrangement of the vertices of a graph where the maximum number of cuts between the edges of the graph and a line separating consecutive vertices is minimized. We first review previous approaches for special classes of graphs, followed by lower bounds and then a linear integer formulation for the general problem. We then propose a branch-and-bound algorithm based on different lower bounds on the cutwidth of partial solutions. Additionally, we introduce a Greedy Randomized Adaptive Search Procedure (GRASP) heuristic to obtain good initial solutions. The combination of the branch-and-bound and GRASP methods results in optimal solutions or a reduced relative gap (difference between upper and lower bounds) on the instances tested. Empirical results with a collection of previously reported instances indicate that the proposed algorithm is able to solve all the small instances (up to 32 vertices) as well as some of the large instances tested (up to 158 vertices) using less than 30 minutes of CPU time. We compare the results of our method with previous lower bounds, and with the best previous linear integer formulation solved using Cplex. Both comparisons favor the proposed procedure.

Scatter search particle filter for 2d real-time hands and face tracking

This paper presents the scatter search particle filter (SSPF) algorithm and its application to real-time hands and face tracking. SSPF combines sequential Monte Carlo (particle filter) and combinatorial optimization (scatter search) methods. Hands and face are characterized using a skin-color model based on explicit RGB region definition. The hybrid SSPF approach enhances the performance of classical particle filter, reducing the required evaluations of the weighting function and increasing the quality of the estimated solution. The system operates on 320x240 live video in real-time.