Large scale SLAM with visual features

Comunicación presentada en el X Workshop of Physical Agents, Cáceres, 10-11 septiembre 2009.

Deformable templates for tracking and analysis of intravascular ultrasound sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

A study of 2D features for 3D visual SLAM

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

Environmental and Occupational Optometry. Lesson 8 (Course 2013-2014)

Tema 8: Pantallas de visualización de datos. Actividad voluntaria nº 5.

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.

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.

Augmented and Virtual Reality techniques for footwear

The use of 3D imaging techniques has been early adopted in the footwear industry. In particular, 3D imaging could be used to aid commerce and improve the quality and sales of shoes. Footwear customization is an added value aimed not only to improve product quality, but also consumer comfort. Moreover, customisation implies a new business model that avoids the competition of mass production coming from new manufacturers settled mainly in Asian countries. However, footwear customisation implies a significant effort at different levels. In manufacturing, rapid and virtual prototyping is required; indeed the prototype is intended to become the final product. The whole design procedure must be validated using exclusively virtual techniques to ensure the feasibility of this process, since physical prototypes should be avoided. With regard to commerce, it would be desirable for the consumer to choose any model of shoes from a large 3D database and be able to try them on looking at a magic mirror. This would probably reduce costs and increase sales, since shops would not require storing every shoe model and the process of trying several models on would be easier and faster for the consumer. In this paper, new advances in 3D techniques coming from experience in cinema, TV and games are successfully applied to footwear. Firstly, the characteristics of a high-quality stereoscopic vision system for footwear are presented. Secondly, a system for the interaction with virtual footwear models based on 3D gloves is detailed. Finally, an augmented reality system (magic mirror) is presented, which is implemented with low-cost computational elements that allow a hypothetical customer to check in real time the goodness of a given virtual footwear model from an aesthetical point of view.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

Method for targetless tracking subpixel in-plane movements

We present a targetless motion tracking method for detecting planar movements with subpixel accuracy. This method is based on the computation and tracking of the intersection of two nonparallel straight-line segments in the image of a moving object in a scene. The method is simple and easy to implement because no complex structures have to be detected. It has been tested and validated using a lab experiment consisting of a vibrating object that was recorded with a high-speed camera working at 1000 fps. We managed to track displacements with an accuracy of hundredths of pixel or even of thousandths of pixel in the case of tracking harmonic vibrations. The method is widely applicable because it can be used for distance measuring amplitude and frequency of vibrations with a vision system.

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.

Sperimentazione con algoritmi per l'analisi di nuvole di punti per applicazioni di guida autonoma

Al giorno d’oggi quasi tutte le persone possiedono un mezzo motorizzato che utilizzano per spostarsi. Tale operazione, che risulta semplice per una persona, può essere compiuta da un robot o un autoveicolo in modo autonomo? La risposta a questa domanda è si, ma se ad una persona serve solo un po’ di pratica per guidare, questa azione non risulta altrettanto immediata per dei veicoli motorizzati. In soccorso ad essi vi è la Computer Vision, un ramo dell’informatica che, in un certo senso, rende un elaboratore elettronico in grado di percepire l’ambiente circostante, nel modo in cui una persona fa con i propri occhi. Oggi ci concentreremo su due campi della computer vision, lo SLAM o Simultaneous Localization and Mapping, che rende un robot in grado di mappare, attraverso una camera, il mondo in cui si trova ed allo stesso tempo di localizzare, istante per istante, la propria posizione all’interno di esso, e la Plane Detection, che permette di estrapolare i piani presenti all’interno di una data immagine.

Utilizing Depth Information for Emerging 3D Applications

Thesis (Ph.D.)--University of Washington, 2016-06

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.