Librería para el procesamiento de señales digitales con computadora

In this work the basic characteristics of the development and the functionality of a specific software are exposed for the teaching in any matter that includes among their contents the "digital signal processing". The software has allowed that the students of the University of Cordoba that study the studies of Engineer in Automatic ad Electronic Industrial, Technical Engineering in Computer science of Systems and Technical Engineering in Computer science of Administration, they can simulate the theoretical contents corresponding to matters with the thematic one commented previously, without necessity of requiring additional computer of the University. The main advantage of the developed product rests in the limitation of the time doesn't exceed the three hours, keeping in mind that for these ends the software can be used without necessity of having programming knowledge. In the environment of the investigation, it can be used as development platform, beings necessary to have programming knowledge in the language C++.En este trabajo se exponen las características básicas del desarrollo y la funcionalidad de un software específico para la enseñanza en cualquier materia qeu incluya entre sus contenidos el "procesamiento digital de señales". El software ha permitido que los alumnos de la Universidad de Córdoba que cursan los estudios de Ingeniero en Automática y Electrónica Industrial, Ingeniería Técnica en Informática de Sistemas e Ingeniería Técnica en Informática de Gestión, puedan simular los contenidos teóricos correspondientes a materias con la temática comentada anteriormente, sin necesidad de requerir adicionales medios informáticos de la Universidad.La principal ventaja del producto desarrollado estriba en la limitación del tiempo requerido para su aprendizaje. En el ámbito de la enseñanza práctica, ha sido comprobado que este tiempo no excede las tres horas, teniendo en cuenta que para estos fines el software puede ser utilizado sin necesidad de tener conocimientos de programación. En el ámbito de la investigación, puede ser utilizado como plataforma de desarrollo, siendo necesario tener conocimientos de programación en el lenguaje C++

Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture

Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one

Multi-view gait recognition on curved

Appearance changes due to viewing angle changes cause difficulties for most of the gait recognition methods. In this paper, we propose a new approach for multi-view recognition, which allows to recognize people walking on curved paths. The recognition is based on 3D angular analysis of the movement of the walking human. A coarse-to-fine gait signature represents local variations on the angular measurements along time. A Support Vector Machine is used for classifying, and a sliding temporal window for majority vote policy is used to smooth and reinforce the classification results. The proposed approach has been experimentally validated on the publicly available “Kyushu University 4D Gait Database”

The AVA Multi-View Dataset for Gait Recognition

In this paper, we introduce a new multi-view dataset for gait recognition. The dataset was recorded in an indoor scenario, using six convergent cameras setup to produce multi-view videos, where each video depicts a walking human. Each sequence contains at least 3 complete gait cycles. The dataset contains videos of 20 walking persons with a large variety of body size, who walk along straight and curved paths. The multi-view videos have been processed to produce foreground silhouettes. To validate our dataset, we have extended some appearance-based 2D gait recognition methods to work with 3D data, obtaining very encouraging results. The dataset, as well as camera calibration information, is freely available for research purpose

Entropy Volumes for Viewpoint Independent Gait Recognition

Gait as biometrics has been widely used for human identi cation. However, direction changes cause di culties for most of the gait recognition systems, due to appearance changes. This study presents an e cient multi-view gait recognition method that allows curved trajectories on completely unconstrained paths for in- door environments. Our method is based on volumet- ric reconstructions of humans, aligned along their way. A new gait descriptor, termed as Gait Entropy Vol- ume (GEnV), is also proposed. GEnV focuses on cap- turing 3D dynamical information of walking humans through the concept of entropy. Our approach does not require the sequence to be split into gait cycles. A GEnV based signature is computed on the basis of the previous 3D gait volumes. Each signature is clas- si ed by a Support Vector Machine, and a majority voting policy is used to smooth and reinforce the clas- si cations results. The proposed approach is experimen- tally validated on the \AVA Multi-View Gait Dataset (AVAMVG)" and on the \Kyushu University 4D Gait Database (KY4D)". The results show that this new ap- proach achieves promising results in the problem of gait recognition on unconstrained paths

Sistema digital de catalogación y consulta de documentos académicos: Tesis, Tesinas, Proyectos de Fin de Carrera

A digital system has been developed on order to catalogue and consult academic documents: Theses, First Degree Dissertations and Technical Degree Final Projects. The system uses a listing of bibliographical subjects which makes easy the catalogue and search of theses documents. The system guarantees the copyright because the document can be consulted, but they can not be printed or copied on external devices. In addition, a cataloguing and search protocol is proposed in order to the system can be correctly used. This system is available to be integrated in the Web information system of the Library of Córdoba University.Se ha desarrollado un sistema digital de catalogación y consulta de documentos académicos: Tesis, Tesinas y Proyectos de fin de carrera. El sistema incorpora un listado de materias bibliográficas que facilitan la catalogación y búsqueda de los documentos. El sistema garantiza la propiedad intelectual de los autores, porque permite que los documentos sean consultados, pero impiden que sean impresos o copiados en dispositivos externos. Además, se propone un protocolo de catalogación y consulta de estos documentos académicos para que el sistema pueda ser correctamente utilizado. Este sistema está disponible para ser integrado en la página Web de la Biblioteca de la Universidad de Córdoba

A novel framework for making dominant point detection methods non-parametric

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves

A new approach for multi-view gait recognition on unconstrained paths

Direction changes cause di culties for most of the gait recognition systems, due to appearance changes. We propose a new approach for multi-view gait recognition, which focuses on recognizing people walking on unconstrained (curved and straight) paths. To this e ect, we present a new rotation invariant gait descriptor which is based on 3D angular analysis of the movement of the subject. Our method does not require the sequence to be split into gait cycles, and is able to provide a response before processing the whole sequence. A Support Vector Machine is used for classifying, and a sliding temporal window with majority vote policy is used to reinforce the classi cation results. The proposed approach has been experimentally validated on \AVA Multi-View Dataset" and \Kyushu University 4D Gait Database" and compared with related state-of-art work. Experimental results demonstrate the e ectiveness of this approach in the problem of gait recognition on unconstrained path

Keypoint descriptor fusion with Dempster-Shafer Theory

Keypoint matching is the task of accurately nding the location of a scene point in two images. Many keypoint descriptors have been proposed in the literature aiming at providing robustness against scale, translation and rotation transformations, each having advantages and disadvantages. This paper proposes a novel approach to fuse the information from multiple keypoint descriptors using Dempster-Shafer Theory of evidence [1], which has proven particularly e cient in combining sources of information providing incomplete, imprecise, biased, and con ictive knowledge. The matching results of each descriptor are transformed into an evidence distribution on which a con dence factor is computed making use of its entropy. Then, the evidence distributions are fused using Dempster-Shafer Theory (DST), considering its con dence. As result of the fusion, a new evidence distribution that improves the result of the best descriptor is obtained. Our method has been tested with SIFT, SURF, ORB, BRISK and FREAK descriptors using all possible combinations of them. Results on the Oxford keypoint dataset [2] shows that the proposed approach obtains an improvement of up to 10% compared to the best one (FREAK)

A new thresholding approach for automatic generation of polygonal approximations

The present paper proposes a new algorithm for automatic generation of polygonal approximations of 2D closed contours based on a new thresholding method. The new proposal computes the signi cance level of the contour points using a new symmetric version of the well-known Ramer, Douglas - Peucker method, and then a new Adaptive method is applied to threshold the normalized signi cance level of the contour points to generate the polygonal approximation. The experiments have shown that the new algorithm has good performance for generating polygonal approximations of 2D closed contours. Futhermore, the new algorithm does not require any parameter to be tuned