Mario Hernández-Tejera

Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision

A solution based on a swarm intelligence metaphor with a prey-predator scheme is proposed for real time object tracking in video sequences, which is a basic process in multiple computer vision tasks. Swarm predator particles fly on a Boid-like fashion over image prey pixels, using combined image features to guide individual movement rules. Object tracking emerges from interaction between predator particles and their environment. The paper includes methods description and experimental evaluations on video streams that illustrate the efficiency of swarm based methods in different vision tasks

Software Agents as Cloud Computing Services

The community devoted to the agency theory needs to create practical solutions capable of representing users in the virtual societies which are emerging as a result of Internet. Cloud Computing has precisely succeeded as a model able to bring software solutions to users in a practical and transparent manner. To foster the adoption of agent-based solutions by the users, this paper describes a model where software agents figure as a new Cloud Computing service which would represent clients in virtual environments. We discuss the challenges that entail the proposal, the technologies necessary for its implementation and finally we develop a proof of concept to confirm its viability.

Heuristic algorithms for fast and accurate tracking of moving objects in unrestricted environments

Tracking of objects is a basic process in computer vision. This process can be formulated as exploration problems and thus can be expressed as a search into a states space based representation approach. However, these problems are hard of solving because they involve search through a high dimensional space corresponding to the possible motion and deformation of the object. In this paper, we propose a heuristic algorithm that combines three features in order to compute motion efficiently: (1) a quality of function match, (2) Kullback-Leibler measure as heuristic to guide the search process and (3) incorporation of target dynamics for computing promising search alternatives. Once target 2D motion has been calculated, the result value of the quality of function match computed is used in other heuristic algorithm with the purpose of verifying template updates. Also, a short-term memory subsystem is included with the purpose of recovering previous views of the target object. The paper includes experimental evaluations with video streams that illustrate the efficiency for real-time vision based-tasks.

Challenges of using smart local devices for the management of the Smart Grid

The Smart Grid is an important milestone for the development of modern societies, which have increased their energy demand and largely depends on a reliable power supply. The management of the Smart Grid requires the installation of distributed and reactive control systems that are able to represent the interests of users, manage the demand on real time and operate the renewable energy sources safely and efficiently. To this end, one of the most accepted models is based on smart local devices that bargain the activity of the production and consumption units on behalf of users. However, the adoption of this technology poses important challenges that have not been considered in depth so far, and that will affect the efficiency, flexibility and reliability of the grid. This paper analyzes these challenges and puts forward the basis of a new model that aims to solve them.

AddCanny: edge detector for video processing

In this paper, we present AddCanny, an Anisotropic Diffusion and Dynamic reformulation of the Canny edge detector. The proposal provides two modifications to classical Canny detector. The first one consists of using an anisotropic diffusion filter instead of a Gaussian filter as Canny does in order to obtain better edge detection and location. The second one is the replacement of the hysteresis step by a dynamic threshold process, in order to reduce blinking effect of edges during successive frames and, therefore, generate more stable edges in sequences. Also, a new performance measurement based on the Euclidean Distance Transform to evaluate the consistency of computed edges is proposed. The paper includes experimental evaluations with different video streams that illustrate the advantages of AddCanny compared to classical Canny edge detector.

Analysis of Relevant Maxima in Distance Transform. An Application to Fast Coarse Image Segmentation

The Distance Transform is a powerful tool that has been used in many computer vision tasks. In this paper, the use of relevant maxima in distance transforms medial axis is proposed as a method for fast image data reduction. These disc-shaped maxima include morphological information from the object they belong to, and because maxima are located inside homogeneous regions, they also sum up chromatic information from the pixels they represent. Thus, maxima can be used instead of single pixels in algorithms which compute relations among pixels, effectively reducing computation times. As an example, a fast method for color image segmentation is proposed, which can also be used for textured zones detection. Comparisons with mean shift segmentation algorithm are shown.

Distance maps from unthresholded magnitudes

A straightforward algorithm that computes distance maps from unthresholded magnitude values is presented, suitable for still images and video sequences. While results on binary images are similar to classic Euclidean Distance Transforms, the proposed approach does not require a binarization step. Thus, no thresholds are needed and no information is lost in intermediate classification stages. Experiments include the evaluation of segmented images using the watershed algorithm and the measurement of pixel value stability in video sequences.

Tracking Deformable Objects with Evolving Templates for Real-Time Machine Vision

A tracking approach not only needs to matching target objects in dynamic scenes, it also needs to update templates when it is required, computing occlusions, and processing multiple-objects with real-time performance. In this Chapter, we present an heuristic search algorithm with target dynamics to match target objects with real-time performance on general purpose hardware. The results of this heuristic search are combined with the more common views of target objects, and intensity information in order to update the templates. As a result, the updating process will be computed only when the target object has evolved to a transformed shape dissimilar with respect to the current shape, providing robust tracking, and multiobject tracking because accurate template updating is performed. The paper includes experimental results with inside and outside video streams demonstrating the effectiveness and efficiency for real-time machine vision based tasks in unrestricted environments.

Heuristic algorithm for visual tracking of deformable objects

Abstract Many vision problems require fast and accurate tracking of objects in dynamic scenes. These problems can be formulated as exploration problems and thus can be expressed as a search into a state space based approach. However, these problems are hard to solve because they involve search through a space of transformations corresponding to all the possible motion and deformation. In this paper, we propose a heuristic algorithm through the space of transformations for computing target 2D motion. Three features are combined in order to compute efficient motion: (1) a quality of function match based on a holistic similarity measurement, (2) Kullback–Leibler measure as heuristic to guide the search process and (3) incorporation of target dynamics into the search process for computing the most promising search alternatives. Once 2D motion has been calculated, the result value of the quality of function match computed is used with the purpose of verifying template updates. A template will be updated only when the target object has evolved to a transformed shape dissimilar with respect to the actual shape. Also, a short-term memory subsystem is included with the purpose of recovering previous views of the target object. The paper includes experimental evaluations with video streams that illustrate the efficiency and suitability for real-time vision based tasks in unrestricted environments.

Heuristic algorithm for computing fast template motion in video streams

Many vision problems require computing fast template motion in dynamic scenes. These problems can be formulated as exploration problems and thus can be expressed as a search into a state space based representation approach. However, these problems are hard to solve because they involve search through a high dimensional space. In this paper, we propose a heuristic algorithm through the space of transformations for computing target 2D motion. Three features are combined in order to compute efficient motion: (1) a quality of function match based on a holistic similarity measurement, (2) Kullback-Leibler measure as heuristic to guide the search process and (3) incorporation of target dynamics into the search process for computing the most promising search alternatives. The paper includes experimental evaluations that illustrate the efficiency and suitability for real-time vision based tasks.