Portable 3D laser-camera calibration system with color fusion for SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR2012), Taipei, Taiwan, Aug. 29-31, 2012.

Alteraciones oculares y visuales en personas que trabajan con ordenador y son usuarias de lentes de contacto: una revisión bibliográfica

Fundamentos: El elevado número de personas que trabajando con ordenador utiliza lentes de contacto plantea la cuestión sobre si la suma de estos dos factores de riesgo para la salud visual puede originar un agravamiento del Síndrome Visual Informático. El objetivo de esta revisión es sintetizar el conocimiento científico sobre las alteraciones oculares y visuales relacionadas con la exposición a ordenador en usuarios de lentes de contacto. Métodos: Revisión de artículos científicos (2003-2013) en español e inglés, realizando una búsqueda bibliográfica, en Medline a través de PubMed y en Scopus. Resultados: La búsqueda inicial aportó 114 trabajos, después de aplicar criterios de inclusión/exclusión se incluyeron seis artículos. Todos ellos ponen de manifiesto que las alteraciones al utilizar el ordenador son más frecuentes en las personas usuarias de lentes de contacto, con prevalencias que oscilan de 95,0% al 16,9% que en las que no utilizan lentes de contacto, cuya prevalencia va del 57,5% al 9,9% y con una probabilidad cuatro veces mayor de padecer ojo seco [OR: 4,07 (IC 95%: 3,52-4,71)]. Conclusiones: Las personas usuarias de ordenador padecen más alteraciones oculares y visuales cuando además son usuarias de lentes de contacto, pero los estudios son escasos y poco contundentes. Se precisan nuevas investigaciones que analicen la influencia según los tipos de lentes y sus condiciones de uso, tanto en la sintomatología como en la calidad de la lágrima y la superficie ocular. Las lentes de hidrogel de silicona son las que se asocian a mayor confort.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.

Interfaces for Intelligent Environments

Presentaciones de la asignatura Interfaces para Entornos Inteligentes del Máster en Tecnologías de la Informática/Machine Learning and Data Mining.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

Evaluation of Binocular Vision Therapy Efficacy by 3D Video-Oculography Measurement of Binocular Alignment and Motility

Objective: To evaluate two cases of intermittent exotropia (IX(T)) treated by vision therapy the efficacy of the treatment by complementing the clinical examination with a 3-D video-oculography to register and to evidence the potential applicability of this technology for such purpose. Methods: We report the binocular alignment changes occurring after vision therapy in a woman of 36 years with an IX(T) of 25 prism diopters (Δ) at far and 18 Δ at near and a child of 10 years with 8 Δ of IX(T) in primary position associated to 6 Δ of left eye hypotropia. Both patients presented good visual acuity with correction in both eyes. Instability of ocular deviation was evident by VOG analysis, revealing also the presence of vertical and torsional components. Binocular vision therapy was prescribed and performed including different types of vergence, accommodation, and consciousness of diplopia training. Results: After therapy, excellent ranges of fusional vergence and a “to-the-nose” near point of convergence were obtained. The 3-D VOG examination (Sensoro Motoric Instruments, Teltow, Germany) confirmed the compensation of the deviation with a high level of stability of binocular alignment. Significant improvement could be observed after therapy in the vertical and torsional components that were found to become more stable. Patients were very satisfied with the outcome obtained by vision therapy. Conclusion: 3D-VOG is a useful technique for providing an objective register of the compensation of the ocular deviation and the stability of the binocular alignment achieved after vision therapy in cases of IX(T), providing a detailed analysis of vertical and torsional improvements.