A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Vehicle Type Detection by Convolutional Neural Networks

In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Road pollution estimation using static cameras and neural networks

Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de carreteras de cualquier estado o país para estimar la contaminación en cada área. Esta propuesta utiliza redes neuronales de aprendizaje profundo para la detección de objetos, y un modelo de estimación de contaminación basado en la frecuencia de vehículos y su velocidad. Los experimentos muestran prometedores resultados que sugieren que el sistema se puede usar en solitario o combinado con los sistemas existentes para medir la contaminación en carreteras.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Homography estimation with deep convolutional neural networks by random color transformations

Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Foreground object detection enhancement by adaptive super resolution for video surveillance

Foreground object detection is a fundamental low level task in current video surveillance systems. It is usually accomplished by keeping a model of the background at each frame pixel. Many background learning algorithms have difficulties to attain real time operation when applied directly to the output of state of the art high resolution surveillance cameras, due to the large number of pixels. Here we propose a strategy to address this problem which consists in maintaining a low resolution model of the background which is upscaled by adaptive super resolution in order to produce a foreground detection mask of the same size as the original input frame. Extensive experimental results demonstrate the suitability of our proposal, in terms of reduction of the computational load and foreground detection accuracy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Neural Controller for PTZ cameras based on nonpanoramic foreground detection

Abstract—In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a nonparametric foreground detection subsystem. Thus, our aim is to optimize the movements of the PTZ camera to attain the maximum coverage of the observed scene in presence of moving objects. A growing neural gas (GNG) is applied to enhance the representation of the foreground objects. Both qualitative and quantitative results are reported using several widely used datasets, which demonstrate the suitability of our approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks

The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Vehicle Classification in Traffic Environments Using the Growing Neural Gas

Traffic monitoring is one of the most popular applications of automated video surveillance. Classification of the vehicles into types is important in order to provide the human traffic controllers with updated information about the characteristics of the traffic flow, which facilitates their decision making process. In this work, a video surveillance system is proposed to carry out such classification. First of all, a feature extraction process is carried out to obtain the most significant features of the detected vehicles. After that, a set of Growing Neural Gas neural networks is employed to determine their types. A qualitative and quantitative assessment of the proposal is carried out on a set of benchmark traffic video sequences, with favorable results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Effect of the presence of the plant growth promoting rhizobacterium (PGPR) Chryseobacterium balustinum Aur9 and salt stress in the pattern of flavonoids exuded by soybean roots

In this work we studied how biotic and abiotic stresses can alter the pattern of flavonoids exuded by Osumi soybean roots. A routine method was developed for the detection and characterization of the flavonoids present in soybean root exudates using HPLC-MS/MS. Then, a systematic screening of the flavonoids exuded under biotic stress, the presence of a plant growth promoting rhizobacterium, and salt stress was carried out. Results obtained indicate that the presence of Chryseobacterium balustinum Aur9 or 50 mM NaCl changes qualitatively the pattern of flavonoids exuded when compared to control conditions. Thus, in the presence of C. balustinum Aur9, soybean roots did not exude quercetin and naringenin and, under salt stress, flavonoids daidzein and naringenin could not be detected. Soybean root exudates obtained under saline conditions showed a diminished capacity to induce the expression of the nodA gene in comparison to the exudates obtained in the absence of salt. Moreover, lipochitooligosaccharides (LCOs) were not detected or weakly detected when Sinorhizobium fredii SMH12 was grown in the exudates obtained under salt stress conditions or under salt stress in the presence of C. balustinum Au9, respectively.Fil: Dardanelli, Marta Susana. Universidad de Sevilla. Facultad de Farmacia; España. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Biología Molecular. Sección Química Biológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Manyani, Hamid. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: González Barroso, Sergio. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: Rodríguez Carvajal, Miguel A.. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: Gil Serrano, Antonio M.. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: Espuny, Maria R.. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: López Baena, Francisco Javier. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: Bellogín, Ramon A.. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: Megías, Manuel. Universidad de Sevilla. Facultad de Farmacia; EspañaFil: Ollero, Francisco J.. Universidad de Sevilla. Facultad de Farmacia; Españ

Chronic obstructive pulmonary disease in high resolution health care center

Objetivos: a) Describir el perfil clínico del paciente con enfermedad pulmonar obstructiva crónica (EPOC) atendido en una Unidad de Hospitalización polivalente y, b) definir las características clínicas y funcionales que determinan la enfermedad según la fase evolutiva. Pacientes y métodos: estudio prospectivo; período de mayo de 2008 a enero 2011. Ámbito: Unidad polivalente del Centro de Alta Resolución el Toyo. Almería. Casuística: pacientes con EPOC atendidos en el Servicio de Urgencias perteneciente a la Unidad Polivalente. Metodología: hoja de recogida de datos con las variables sociodemográficas, clínicas, biológicas y terapéuticas. Para el método estadístico se ha utilizado el análisis descriptivo de las variables. Resultados: Se han incluido 224 pacientes, edad media de 74 años; (90% varones); el 85.1% en tratamiento previo con la combinación de glucocorticoides y broncodilatadores de acción prolongada; 46.8% eran fumadores activos, en 52.2% estancia observacional y 32% en estancia corta. El 93.2% presentó aumento de disnea; el 53.2% presentó fiebre, 45% cianosis y el 59.5 % que mostraban condensación pulmonar permanecen en la corta estancia hospitalaria. Conclusiones: algunas características clínico-biológicas y funcionales permiten diferenciar a cada grupo y predecir la estancia hospitalaria.Objective: To describe the clinical profile of the patient with Chronic Obstructive Pulmonary Disease (COPD) admitted for hospitalization to the Multi Purpose Service (MPS)of the hospital and to report the clinical and functional characteristics of COPD at the different phases of the disease. Patients and methods: A prospective study was performed between May 2008 and January 2011, in the setting of the Multi Purpose Service of the Centre of High Resolution of El Toyo, Almería. We studied patients with COPD admitted to the Emergency Service of the MPS of the hospital. We used a data collection form with all the sociodemographic, clinical, biological, and therapeutic variables of the patients. Statistical analysis was carried out using the descriptive analysis of the variables. Results: A total of 224 patients were studied, with a mean age of 74 years (90% of them were men); 85% of the patients were under previous treatment with a combined therapy of glucocorticoids plus long- acting bronchodilators; 46.8% were smokers; 52.2% were in observation stay, and 32% of the patients stayed at short stay at the hospital; 93.2% of the patients showed an increased dypnea; 53.2% presented fever; 45% had cyanosis, and 59.5% which showed condensation stayed at short stay at the hospital. Conclusions: Some of the clinical, biological and functional characteristics of the patients allowed us to differentiate each group and to predict the lengh of the hospital stay