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.

Virtual reconstruction of the 400 toneladas nao from Diego García de Palacio’s Instrucción náutica (1587)

The period between 1570-1620 has left a remarkable amount of documents related to shipbuilding in the Iberian Peninsula. Among them, the Instrucción nautica written by Diego García de Palacio in 1587 is widely recognized as the first published book that includes an extensive discussion of ship design and construction. García de Palacio centres his discussion on a 400 toneladas nao, a series of woodcuts that illustrate the shape and dimensions of the ship accompany the explanation. In the late XVI century ship hulls were designed following procedures based upon an old shipwrightry tradition born in the Mediterranean. By simple rules the master shipwright plots the central frame and tail frames and complete the hull body using wooden ribbands. Computer software for 3D modelling using NURBs surfaces helps to recreate ships hulls. In this work the 400 toneladas nao is reconstructed and her hydrostatic parameters are compared with other ships.

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.

Geometric 3D point cloud compression

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment but usually the huge amount of 3D information is unmanageable by the robot storage and computing capabilities. A data compression is necessary to store and manage this information but preserving as much information as possible. In this paper, we propose a 3D lossy compression system based on plane extraction which represent the points of each scene plane as a Delaunay triangulation and a set of points/area information. The compression system can be customized to achieve different data compression or accuracy ratios. It also supports a color segmentation stage to preserve original scene color information and provides a realistic scene reconstruction. The design of the method provides a fast scene reconstruction useful for further visualization or processing tasks.

3D Maps Representation Using GNG

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

User's manual for the CAL-3D user convenience package. Volume II - program listings. Final technical report.

National Highway Traffic Safety Administration, Washington, D.C.

Arbitrary real-time three-dimensional corporal object sensing and reconstruction scheme

A real-time three-dimensional (3D) object sensing and reconstruction scheme is presented that can be applied on any arbitrary corporeal shape. Operation is demonstrated on several calibrated objects. The system uses curvature sensors based upon in-line fiber Bragg gratings encapsulated in a low-temperature curing synthetic silicone. New methods to quantitatively evaluate the performance of a 3D object-sensing scheme are developed and appraised. It is shown that the sensing scheme yields a volumetric error of 1% to 9%, depending on the object.

Glare, a GIS tool to reconstruct the 3D surface of palaeoglaciers

Acknowledgements This research has been supported by the Leverhulme Trust International Network Grant IN-2012-140. Processing and collecting of ground penetrating data in Forgefonna was part of Elend Førre's master's project that was completed in 2009 at the Department of Geography, University of Bergen. We also acknowledge Dr Andreas Bauder for providing the subglacial topography data for Griessgletscher and Simone Tarquini for granting access to the high resolution TIN of Italy, a cut of which is provided to the reader to practice the tools (see Appendix). Referees Dr. Iestyn Barr, Dr. Jeremy Ely and Dr. Marc Oliva are thanked for their constructive comments and tool testing, which significantly improved the final output.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

Algorithme de reconstruction itératif pour tomographie optique diffuse avec mesures dans le domaine temporel

L'imagerie par tomographie optique diffuse requiert de modéliser la propagation de la lumière dans un tissu biologique pour une configuration optique et géométrique donnée. On appelle cela le problème direct. Une nouvelle approche basée sur la méthode des différences finies pour modéliser numériquement via l'équation de la diffusion (ED) la propagation de la lumière dans le domaine temporel dans un milieu inhomogène 3D avec frontières irrégulières est développée pour le cas de l'imagerie intrinsèque, c'est-à-dire l'imagerie des paramètres optiques d'absorption et de diffusion d'un tissu. Les éléments finis, lourds en calculs, car utilisant des maillages non structurés, sont généralement préférés, car les différences finies ne permettent pas de prendre en compte simplement des frontières irrégulières. L'utilisation de la méthode de blocking-off ainsi que d'un filtre de Sobel en 3D peuvent en principe permettre de surmonter ces difficultés et d'obtenir des équations rapides à résoudre numériquement avec les différences finies. Un algorithme est développé dans le présent ouvrage pour implanter cette approche et l'appliquer dans divers cas puis de la valider en comparant les résultats obtenus à ceux de simulations Monte-Carlo qui servent de référence. L'objectif ultime du projet est de pouvoir imager en trois dimensions un petit animal, c'est pourquoi le modèle de propagation est au coeur de l'algorithme de reconstruction d'images. L'obtention d'images requière la résolution d'un problème inverse de grandes dimensions et l'algorithme est basé sur une fonction objective que l'on minimise de façon itérative à l'aide d'une méthode basée sur le gradient. La fonction objective mesure l'écart entre les mesures expérimentales faites sur le sujet et les prédictions de celles-ci obtenues du modèle de propagation. Une des difficultés dans ce type d'algorithme est l'obtention du gradient. Ceci est fait à l'aide de variables auxiliaire (ou adjointes). Le but est de développer et de combiner des méthodes qui permettent à l'algorithme de converger le plus rapidement possible pour obtenir les propriétés optiques les plus fidèles possible à la réalité capable d'exploiter la dépendance temporelle des mesures résolues en temps, qui fournissent plus d'informations tout autre type de mesure en TOD. Des résultats illustrant la reconstruction d'un milieu complexe comme une souris sont présentés pour démontrer le potentiel de notre approche.

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.

Algoritmos de reconstrucción de superficies 3D para modelaje antropométrico

Los protocolos de medición antropométrica se caracterizan por la profusión de medidas discretas o localizadas, en un intento para caracterizar completamente la forma corporal del sujeto -- Dichos protocolos se utilizan intensivamente en campos como medicina deportiva, forense y/o reconstructiva, diseño de prótesis, ergonomía, en la confección de prendas, accesorios, etc -- Con el avance de algoritmos de recuperación de formas a partir de muestreos (digitalizaciones) la caracterización antropométrica se ha alterado significativamente -- El articulo presente muestra el proceso de caracterización digital de forma corpórea, incluyendo los protocolos de medición sobre el sujeto, el ambiente computacional - DigitLAB- (desarrollado en el CII-CAD-CAM-CG de la Universidad EAFIT) para recuperación de superficies, hasta los modelos geométricos finales -- Se presentan comparaciones de los resultados obtenidos con DigitLAB y con paquetes comerciales de recuperación de forma 3D -- Los resultados de DigitLAB resultan superiores, debido principalmente al hecho de que este toma ventaja de los patrones de las digitalizaciones (planares de contacto, por rejilla de pixels - range images -, etc.) y provee módulos de tratamiento geométrico - estadístico de los datos para poder aplicar efectivamente los algoritmos de recuperación de forma -- Se presenta un caso de estudio dirigido a la industria de la confección, y otros efectuados sobre conjuntos de prueba comunes en el ámbito científico para la homologación de algoritmos

Sviluppo di un sistema di Realta Aumentata applicato a un modello 3D della prostata, sviluppato a partire da immagini RM, al fine di facilitare la dissezione del tumore e la conservazione dei fasci neuro vascolari durante l'intervento chirurgico di prostatectomia radicale robot-assistita

La realtà aumentata (AR) è una nuova tecnologia adottata in chirurgia prostatica con l'obiettivo di migliorare la conservazione dei fasci neurovascolari (NVB) ed evitare i margini chirurgici positivi (PSM). Abbiamo arruolato prospetticamente pazienti con diagnosi di cancro alla prostata (PCa) sul base di biopsia di fusione mirata con mpMRI positiva. Prima dell'intervento, i pazienti arruolati sono stati indirizzati a sottoporsi a ricostruzione del modello virtuale 3D basato su mpMRI preoperatoria immagini. Infine, il chirurgo ha eseguito la RARP con l'ausilio del modello 3D proiettato in AR all'interno della console robotica (RARP guidata AR-3D). I pazienti sottoposti a AR RARP sono stati confrontati con quelli sottoposti a "RARP standard" nello stesso periodo. Nel complesso, i tassi di PSM erano comparabili tra i due gruppi; I PSM a livello della lesione indice erano significativamente più bassi nei pazienti riferiti al gruppo AR-3D (5%) rispetto a quelli nel gruppo di controllo (20%; p = 0,01). La nuova tecnica di guida AR-3D per l'analisi IFS può consentono di ridurre i PSM a livello della lesione dell'indice