Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space

This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects

Real-time shot detection based on motion analysis and multiple low-level techniques

To index, search, browse and retrieve relevant material, indexes describing the video content are required. Here, a new and fast strategy which allows detecting abrupt and gradual transitions is proposed. A pixel-based analysis is applied to detect abrupt transitions and, in parallel, an edge-based analysis is used to detect gradual transitions. Both analysis are reinforced with a motion analysis in a second step, which significantly simplifies the threshold selection problem while preserving the computational requirements. The main advantage of the proposed system is its ability to work in real time and the experimental results show high recall and precision values

Kernel bandwidth estimation for moving object detection in non-stabilized cameras

The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences

Moving Object Detection for Real-Time High-Quality Lightweight Applications on Smart Cameras

Here, a novel and efficient strategy for moving object detection by non-parametric modeling on smart cameras is presented. Whereas the background is modeled using only color information, the foreground model combines color and spatial information. The application of a particle filter allows the update of the spatial information and provides a priori information about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation result

Temporal segmentation tool for high-quality real time video editing software

The increasing use of video editing software requires faster and more efficient editing tools. As a first step, these tools perform a temporal segmentation in shots that allows a later building of indexes describing the video content. Here, we propose a novel real-time high-quality shot detection strategy, suitable for the last generation of video editing software requiring both low computational cost and high quality results. While abrupt transitions are detected through a very fast pixel-based analysis, gradual transitions are obtained from an efficient edge-based analysis. Both analyses are reinforced with a motion analysis that helps to detect and discard false detections. This motion analysis is carried out exclusively over a reduced set of candidate transitions, thus maintaining the computational requirements demanded by new applications to fulfill user needs

Un acercamiento funcional a la resolución de desigualdades matemáticas

Recientemente investigadores en matemática educativa han señalado un concepto que representa un reto en el proceso enseñanza-aprendizaje: la resolución de desigualdades matemáticas. En efecto, múltiples artículos de investigación (Bazzini &Tsamir, 2001; Boero & Bazzini, 2004; Linchevski & Sfard, 1991; Tsamir & Almog, 1999, et al.) identifican dificultades en el proceso enseñanza aprendizaje de este concepto y proponen acercamientos que buscan promover una mejora en su enseñanza y aprendizaje. El presente trabajo apunta en esta dirección, se propone establecer un acercamiento funcional a la resolución de desigualdades matemáticas y bajo el marco didáctico Cuevas & Pluvinage diseñar actividades apoyándose en el uso de las tecnologías digitales; con todo esto hemos formulado una propuesta de enseñanza que tiene como objetivo favorecer un aprendizaje significativo de este concepto. Adicionalmente proporcionamos datos de experiencia en el aula de nuestra propuesta

Introducción al concepto de derivada de una función real con apoyo de las tecnologías digitales

Este artículo presenta una propuesta para introducir el concepto de derivada de una función real en un primer curso de cálculo diferencial a nivel superior. Para el desarrollo de esta propuesta se han tomado elementos del desarrollo histórico conceptual del cálculo, la aplicación de una ingeniería didáctica en particular y de manera importante el empleo de la tecnología digital. Los resultados obtenidos son alentadores, porque se logró que la mayoría de los estudiantes alcanzaron con éxito un grado de comprensión conceptual y operativa del concepto de derivada de una función real. Lo anterior fue uno de los factores que condujo a elevar sustancialmente el índice de aprobación, ayudando con esto a solventar algunos de los problemas de deserción en la universidad donde se experimentó, pero sobre todo a una mejor comprensión del concepto de derivada de una función real por parte de los estudiantes

Segmentación de Secuencias de Vídeo: análisis y desarrollo de estrategias para la detección de cambios de toma y para la detección de objetos móviles

The important technological advances experienced along the last years have resulted in an important demand for new and efficient computer vision applications. On the one hand, the increasing use of video editing software has given rise to a necessity for faster and more efficient editing tools that, in a first step, perform a temporal segmentation in shots. On the other hand, the number of electronic devices with integrated cameras has grown enormously. These devices require new, fast, and efficient computer vision applications that include moving object detection strategies. In this dissertation, we propose a temporal segmentation strategy and several moving object detection strategies, which are suitable for the last generation of computer vision applications requiring both low computational cost and high quality results. First, a novel real-time high-quality shot detection strategy is proposed. While abrupt transitions are detected through a very fast pixel-based analysis, gradual transitions are obtained from an efficient edge-based analysis. Both analyses are reinforced with a motion analysis that allows to detect and discard false detections. This analysis is carried out exclusively over a reduced amount of candidate transitions, thus maintaining the computational requirements. On the other hand, a moving object detection strategy, which is based on the popular Mixture of Gaussians method, is proposed. This strategy, taking into account the recent history of each image pixel, adapts dynamically the amount of Gaussians that are required to model its variations. As a result, we improve significantly the computational efficiency with respect to other similar methods and, additionally, we reduce the influence of the used parameters in the results. Alternatively, in order to improve the quality of the results in complex scenarios containing dynamic backgrounds, we propose different non-parametric based moving object detection strategies that model both background and foreground. To obtain high quality results regardless of the characteristics of the analyzed sequence we dynamically estimate the most adequate bandwidth matrices for the kernels that are used in the background and foreground modeling. Moreover, the application of a particle filter allows to update the spatial information and provides a priori knowledge about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results. Additionally, we propose the use of an innovative combination of chromaticity and gradients that allows to reduce the influence of shadows and reflects in the detections

Thermal discrete dipole approximation for near-field radiative heat transfer in many-body systems with arbitrary nonreciprocal bodies

The theoretical study of many-body effects in the context of near-field radiative heat transfer (NFRHT) has already led to the prediction of a plethora of thermal radiation phenomena. Special attention has been paid to nonreciprocal systems in which the lack of the Lorentz reciprocity has been shown to give rise to unique physical effects. However, most of the theoretical work in this regard has been carried out with the help of approaches that consider either pointlike particles or highly symmetric bodies (such as spheres), which are not easy to realize and explore experimentally. In this work we develop a many-body approach based on the thermal discrete dipole approximation (TDDA) that is able to describe the NFRHT between nonreciprocal objects of arbitrary size and shape. We illustrate the potential and the relevance of this approach with the analysis of two related phenomena, namely the existence of persistent thermal currents and the photon thermal Hall effect, in a system with several magneto-optical bodies. Our many-body TDDA approach paves the way for closing the gap between experiment and theory that is hindering the progress of the topic of NFRHT in many-body systemsJ.C.C. acknowledges funding from the Spanish Ministry of Science and Innovation (Grant No. PID2020-114880GB-I00

Interplay Between Yu-Shiba-Rusinov States and Multiple Andreev Reflections

Motivated by recent scanning tunneling microscopy experiments on single magnetic impurities on superconducting surfaces, we present here a comprehensive theoretical study of the interplay between Yu-Shiba-Rusinov bound states and (multiple) Andreev reflections. Our theory is based on a combination of an Anderson model with broken spin degeneracy and nonequilibrium Green's function techniques that allows us to describe the electronic transport through a magnetic impurity coupled to superconducting leads for arbitrary junction transparency. Using this combination we are able to elucidate the different tunneling processes that give a significant contribution to the subgap transport. In particular, we predict the occurrence of a large variety of Andreev reflections mediated by Yu-Shiba-Rusinov bound states that clearly differ from the standard Andreev processes in non-magnetic systems. Moreover, we provide concrete guidelines on how to experimentally identify the subgap features originating from these tunneling events. Overall, our work provides new insight into the role of the spin degree of freedom in Andreev transport physics.Comment: 15 pages, 10 figure