Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms.

Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering

Recent advances in non-destructive imaging techniques, such as X-ray computed tomography (CT), make it possible to analyse pore space features from the direct visualisation from soil structures. A quantitative characterisation of the three-dimensional solid-pore architecture is important to understand soil mechanics, as they relate to the control of biological, chemical, and physical processes across scales. This analysis technique therefore offers an opportunity to better interpret soil strata, as new and relevant information can be obtained. In this work, we propose an approach to automatically identify the pore structure of a set of 200-2D images that represent slices of an original 3D CT image of a soil sample, which can be accomplished through non-linear enhancement of the pixel grey levels and an image segmentation based on a PFCM (Possibilistic Fuzzy C-Means) algorithm. Once the solids and pore spaces have been identified, the set of 200-2D images is then used to reconstruct an approximation of the soil sample by projecting only the pore spaces. This reconstruction shows the structure of the soil and its pores, which become more bounded, less bounded, or unbounded with changes in depth. If the soil sample image quality is sufficiently favourable in terms of contrast, noise and sharpness, the pore identification is less complicated, and the PFCM clustering algorithm can be used without additional processing; otherwise, images require pre-processing before using this algorithm. Promising results were obtained with four soil samples, the first of which was used to show the algorithm validity and the additional three were used to demonstrate the robustness of our proposal. The methodology we present here can better detect the solid soil and pore spaces on CT images, enabling the generation of better 2D?3D representations of pore structures from segmented 2D images.

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Improving parameters selection of a seeded region growing method for multiband image segmentation

In the last decade, Object Based Image Analysis (OBIA) has been accepted as an effective method for processing high spatial resolution multiband images. This image analysis method is an approach that starts with the segmentation of the image. Image segmentation in general is a procedure to partition an image into homogenous groups (segments). In practice, visual interpretation is often used to assess the quality of segmentation and the analysis relies on the experience of an analyst. In an effort to address the issue, in this study, we evaluate several seed selection strategies for an automatic image segmentation methodology based on a seeded region growing-merging approach. In order to evaluate the segmentation quality, segments were subjected to spatial autocorrelation analysis using Moran's I index and intra-segment variance analysis. We apply the algorithm to image segmentation using an aerial multiband image.

Optimizing panchromatic image change detection based on change index multiband image analysis

This work proposes an optimization of a semi-supervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.

La domótica al servicio de los enfermos de Alzheimer y sus cuidadores

El cuidado de los enfermos de Alzheimer que viven en su casa se realiza a menudo por cuidadores no profesionales (familiares principalmente) que tienen que compatibilizar sus actividades personales y laborales con el cuidado de sus familiares enfermos. Esta es una tarea que lleva demasiado tiempo, pero que puede ser facilitada por las nuevas tecnologías (especialmente domóticas) que han surgido en los últimos años. El grupo de trabajo DomAlz (que toma su nombre de Domótica y Alzheimer) se ha creado para estudiar el gran abanico de soluciones que existen en el mercado y que se están desarrollando, y proponer nuevas soluciones.

Un algoritmo de replanificación en tiempo real basado en un índice de estabilidad de Lyapunov para líneas de metro

(SPA) En este trabajo, se propone un nuevo índice basado en el método directo de Lyapunov para el diseño de un algoritmo de reprogramación en tiempo real para líneas de metro. En este estudio se utiliza una versión modificada de un modelo de espacio de estados en tiempo real discreto, que considera los efectos de saturación en la línea de metro. Una vez que el modelo de espacio de estados se ha obtenido, el método directo de Lyapunov se aplica con el fin de analizar la estabilidad del sistema de la línea de metro. Como resultado de este análisis no sólo se propone un nuevo índice de estabilidad, sino también la creación de tres zonas de estabilidad para indicar el estado actual del sistema. Finalmente, se presenta un nuevo algoritmo que permite la reprogramación del calendario de los trenes en tiempo real en presencia de perturbaciones medianas. (ENG) A new Lyapunov-based index for designing a rescheduling algorithm in real time for metro lines has been proposed in this paper. A modified real time discrete space state model which considers saturation effects in the metro line has been utilized in this study. Once the space state model has been obtained, the direct method of Lyapunov is applied in order to analyze the stability of the metro line system. As a result of this analysis not only a new stability index is proposed, but also the establishment of three stability zones to indicate the current state of the system. Finally, a new algorithm which allows the rescheduling of the timetable in the real time of the trains under presence of medium disturbances has been presented.

Experimental tests in human-robot collision evaluation and characterization of a new safety index for robot operation

This paper describes an experimental procedure consisting of impact tests that simulate a collision of a human head with an industrial robot with the aim to validate a safety index named as New Index for Robots (NIR) and its outputs. The experiments in this paper are based on lab tests. It is an attempt to characterize the NIR index underlying the main parameters that are involved in crash interaction and to highlight limitations and weakness of suggested impact tests.

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.

CCD image sensor induced error in PIV applications

The readout procedure of charge-coupled device (CCD) cameras is known to generate some image degradation in different scientific imaging fields, especially in astrophysics. In the particular field of particle image velocimetry (PIV), widely extended in the scientific community, the readout procedure of the interline CCD sensor induces a bias in the registered position of particle images. This work proposes simple procedures to predict the magnitude of the associated measurement error. Generally, there are differences in the position bias for the different images of a certain particle at each PIV frame. This leads to a substantial bias error in the PIV velocity measurement (~0.1 pixels). This is the order of magnitude that other typical PIV errors such as peak-locking may reach. Based on modern CCD technology and architecture, this work offers a description of the readout phenomenon and proposes a modeling for the CCD readout bias error magnitude. This bias, in turn, generates a velocity measurement bias error when there is an illumination difference between two successive PIV exposures. The model predictions match the experiments performed with two 12-bit-depth interline CCD cameras (MegaPlus ES 4.0/E incorporating the Kodak KAI-4000M CCD sensor with 4 megapixels). For different cameras, only two constant values are needed to fit the proposed calibration model and predict the error from the readout procedure. Tests by different researchers using different cameras would allow verification of the model, that can be used to optimize acquisition setups. Simple procedures to obtain these two calibration values are also described.

Especificación de arquitecturas ITS. La experiencia del proyecto FOTsis

El constante desarrollo reciente de los Sistemas Inteligentes de Transporte (ITS) Cooperativos ha resultado en diferentes iniciativas que se centran en distintos aspectos del entorno. El proyecto Europeo FOTsis perteneciente al Séptimo Programa Marco de la Comisión Europea (FP7) gira alrededor de la infraestructura del entorno del transporte por carretera. El proyecto tiene como objetivos básicos desplegar y validar 7 servicios que han sido diseñados para maximizar los beneficios de la integración de diferentes entidades basadas en la infraestructura en el paisaje ITS: el operador de infraestructura y proveedores de datos externos, entre otros. Este artículo describe el estado actual del proyecto, destacando la especificación de la arquitectura ITS que lo sustenta: referencias, una breve reseña a la definición de los requisitos de los servicios, y finalmente la propuesta de arquitectura FOTsis, junto con algunas conclusiones sobre las pruebas realizadas sobre la arquitectura propuesta. Al final del artículo se da una visión general de los próximos pasos en el proyecto.

A General Purpose Configurable Controller for Indoors and Outdoors GPS-Denied Navigation for Multirotor Unmanned Aerial Vehicles

This research on odometry based GPS-denied navigation on multirotor Unmanned Aerial Vehicles is focused among the interactions between the odometry sensors and the navigation controller. More precisely, we present a controller architecture that allows to specify a speed specified flight envelope where the quality of the odometry measurements is guaranteed. The controller utilizes a simple point mass kinematic model, described by a set of configurable parameters, to generate a complying speed plan. For experimental testing, we have used down-facing camera optical-flow as odometry measurement. This work is a continuation of prior research to outdoors environments using an AR Drone 2.0 vehicle, as it provides reliable optical flow on a wide range of flying conditions and floor textures. Our experiments show that the architecture is realiable for outdoors flight on altitudes lower than 9 m. A prior version of our code was utilized to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012. The code will be released as an open-source ROS stack hosted on GitHub.

Grasp Mapping Between a 3-Finger Haptic Device and a Robotic Hand

This paper presents the implementation of a robust grasp mapping between a 3-finger haptic device (master) and a robotic hand (slave). Mapping is based on a grasp equivalence defined considering the manipulation capabilities of the master and slave devices. The metrics that translate the human hand gesture to the robotic hand workspace are obtained through an analytical user study. This allows a natural control of the robotic hand. The grasp mapping is accomplished defining 4 control modes that encapsulate all the grasps gestures considered.

Internet-Based Supervisory Teleoperation of a Virtual Humanoid Robot

This paper describes the approach used by the Sarbot-Team for controlling the Atlas humanoid robot during the DARPA Virtual Robotics Challenge that took place in June 2013. Herein we present a proposal for overcoming the restrictions on performance caused by limited bandwidth, high latency and the effects of signal degradation induced by beyond line of sight (BLOS) conditions, RF interference, and other related circumstances. Experimental evaluation confirmed the effectiveness of our approach and present an alternative for coping with constrained communication conditions during the control of humanoid robot deployed at unattended areas.

Integration of low-cost supervisory mobile robots in domestic wireless sensor networks

This paper presents a communication interface between supervisory low-cost mobile robots and domestic Wireless Sensor Network (WSN) based on the Zig Bee protocol from different manufacturers. The communication interface allows control and communication with other network devices using the same protocol. The robot can receive information from sensor devices (temperature, humidity, luminosity) and send commands to actuator devices (lights, shutters, thermostats) from different manufacturers. The architecture of the system, the interfaces and devices needed to establish the communication are described in the paper.