Zooplankton abundance and size structure in the North Atlantic from MSM26_126-10

Data on zooplankton abundance and biovolume were collected in concert with data on the biophysical environment at 9 stations in the North Atlantic, from the Iceland Basin in the East to the Labrador Sea in the West. The data were sampled along vertical profiles by a Laser Optical Plankton Counter (LOPC, Rolls Royce Canada Ltd.) that was mounted on a carousel water sampler together with a Conductivity-Temperature-Depth sensor (CTD, SBE19plusV2, Seabird Electronics, Inc., USA) and a fluorescence sensor (F, ECO Puck chlorophyll a fluorometer, WET Labs Inc., USA). Based on the LOPC data, abundance (individuals/m**3) and biovolume (mm3/m**3) were calculated as described in the LOPC Software Operation Manual [(Anonymous, 2006), http://www.brooke-ocean.com/index.html]. LOPC data were regrouped into 49 size groups of equal log10(body volume) increments, see Edvardsen et al. (2002, doi:10.3354/meps227205). LOPC data quality was checked as described in Basedow et al. (2013, doi:10.1016/j.pocean.2012.10.005). Fluorescence was roughly converted into chlorophyll based on filtered chlorophyll values obtained from station 10 in the Labrador Sea. Due to the low number of filtered samples that was used for the conversion the resulting chlorophyll values should be considered with care. CTD data were screened for erroneous (out of range) values and then averaged to the same frequency as the LOPC data (2 Hz). All data were processed using especially developed scripts in the python programming language. The LOPC is an optical instrument designed to count and measure particles (0.1 to 30 mm equivalent spherical diameter) in the water column, see Herman et al., (2004, doi:10.1093/plankt/fbh095). The size of particles as equivalent spherical diameter (ESD) was computed as described in the manual (Anonymous, 2006), and in more detail in Checkley et al. (2008, doi:10.4319/lo.2008.53.5_part_2.2123) and Gaardsted et al. (2010, doi:10.1111/j.1365-2419.2010.00558.x).

Zooplankton abundance and size structure in the North Atlantic from MSM26_133-7

Data on zooplankton abundance and biovolume were collected in concert with data on the biophysical environment at 9 stations in the North Atlantic, from the Iceland Basin in the East to the Labrador Sea in the West. The data were sampled along vertical profiles by a Laser Optical Plankton Counter (LOPC, Rolls Royce Canada Ltd.) that was mounted on a carousel water sampler together with a Conductivity-Temperature-Depth sensor (CTD, SBE19plusV2, Seabird Electronics, Inc., USA) and a fluorescence sensor (F, ECO Puck chlorophyll a fluorometer, WET Labs Inc., USA). Based on the LOPC data, abundance (individuals/m**3) and biovolume (mm3/m**3) were calculated as described in the LOPC Software Operation Manual [(Anonymous, 2006), http://www.brooke-ocean.com/index.html]. LOPC data were regrouped into 49 size groups of equal log10(body volume) increments, see Edvardsen et al. (2002, doi:10.3354/meps227205). LOPC data quality was checked as described in Basedow et al. (2013, doi:10.1016/j.pocean.2012.10.005). Fluorescence was roughly converted into chlorophyll based on filtered chlorophyll values obtained from station 10 in the Labrador Sea. Due to the low number of filtered samples that was used for the conversion the resulting chlorophyll values should be considered with care. CTD data were screened for erroneous (out of range) values and then averaged to the same frequency as the LOPC data (2 Hz). All data were processed using especially developed scripts in the python programming language. The LOPC is an optical instrument designed to count and measure particles (0.1 to 30 mm equivalent spherical diameter) in the water column, see Herman et al., (2004, doi:10.1093/plankt/fbh095). The size of particles as equivalent spherical diameter (ESD) was computed as described in the manual (Anonymous, 2006), and in more detail in Checkley et al. (2008, doi:10.4319/lo.2008.53.5_part_2.2123) and Gaardsted et al. (2010, doi:10.1111/j.1365-2419.2010.00558.x).

Zooplankton abundance and size structure in the North Atlantic from MSM26_137-17

Data on zooplankton abundance and biovolume were collected in concert with data on the biophysical environment at 9 stations in the North Atlantic, from the Iceland Basin in the East to the Labrador Sea in the West. The data were sampled along vertical profiles by a Laser Optical Plankton Counter (LOPC, Rolls Royce Canada Ltd.) that was mounted on a carousel water sampler together with a Conductivity-Temperature-Depth sensor (CTD, SBE19plusV2, Seabird Electronics, Inc., USA) and a fluorescence sensor (F, ECO Puck chlorophyll a fluorometer, WET Labs Inc., USA). Based on the LOPC data, abundance (individuals/m**3) and biovolume (mm3/m**3) were calculated as described in the LOPC Software Operation Manual [(Anonymous, 2006), http://www.brooke-ocean.com/index.html]. LOPC data were regrouped into 49 size groups of equal log10(body volume) increments, see Edvardsen et al. (2002, doi:10.3354/meps227205). LOPC data quality was checked as described in Basedow et al. (2013, doi:10.1016/j.pocean.2012.10.005). Fluorescence was roughly converted into chlorophyll based on filtered chlorophyll values obtained from station 10 in the Labrador Sea. Due to the low number of filtered samples that was used for the conversion the resulting chlorophyll values should be considered with care. CTD data were screened for erroneous (out of range) values and then averaged to the same frequency as the LOPC data (2 Hz). All data were processed using especially developed scripts in the python programming language. The LOPC is an optical instrument designed to count and measure particles (0.1 to 30 mm equivalent spherical diameter) in the water column, see Herman et al., (2004, doi:10.1093/plankt/fbh095). The size of particles as equivalent spherical diameter (ESD) was computed as described in the manual (Anonymous, 2006), and in more detail in Checkley et al. (2008, doi:10.4319/lo.2008.53.5_part_2.2123) and Gaardsted et al. (2010, doi:10.1111/j.1365-2419.2010.00558.x).

Zooplankton abundance and size structure in the North Atlantic from MSM26_131-4

Data on zooplankton abundance and biovolume were collected in concert with data on the biophysical environment at 9 stations in the North Atlantic, from the Iceland Basin in the East to the Labrador Sea in the West. The data were sampled along vertical profiles by a Laser Optical Plankton Counter (LOPC, Rolls Royce Canada Ltd.) that was mounted on a carousel water sampler together with a Conductivity-Temperature-Depth sensor (CTD, SBE19plusV2, Seabird Electronics, Inc., USA) and a fluorescence sensor (F, ECO Puck chlorophyll a fluorometer, WET Labs Inc., USA). Based on the LOPC data, abundance (individuals/m**3) and biovolume (mm3/m**3) were calculated as described in the LOPC Software Operation Manual [(Anonymous, 2006), http://www.brooke-ocean.com/index.html]. LOPC data were regrouped into 49 size groups of equal log10(body volume) increments, see Edvardsen et al. (2002, doi:10.3354/meps227205). LOPC data quality was checked as described in Basedow et al. (2013, doi:10.1016/j.pocean.2012.10.005). Fluorescence was roughly converted into chlorophyll based on filtered chlorophyll values obtained from station 10 in the Labrador Sea. Due to the low number of filtered samples that was used for the conversion the resulting chlorophyll values should be considered with care. CTD data were screened for erroneous (out of range) values and then averaged to the same frequency as the LOPC data (2 Hz). All data were processed using especially developed scripts in the python programming language. The LOPC is an optical instrument designed to count and measure particles (0.1 to 30 mm equivalent spherical diameter) in the water column, see Herman et al., (2004, doi:10.1093/plankt/fbh095). The size of particles as equivalent spherical diameter (ESD) was computed as described in the manual (Anonymous, 2006), and in more detail in Checkley et al. (2008, doi:10.4319/lo.2008.53.5_part_2.2123) and Gaardsted et al. (2010, doi:10.1111/j.1365-2419.2010.00558.x).

Zooplankton abundance and size structure in the North Atlantic from MSM26_135-10

Data on zooplankton abundance and biovolume were collected in concert with data on the biophysical environment at 9 stations in the North Atlantic, from the Iceland Basin in the East to the Labrador Sea in the West. The data were sampled along vertical profiles by a Laser Optical Plankton Counter (LOPC, Rolls Royce Canada Ltd.) that was mounted on a carousel water sampler together with a Conductivity-Temperature-Depth sensor (CTD, SBE19plusV2, Seabird Electronics, Inc., USA) and a fluorescence sensor (F, ECO Puck chlorophyll a fluorometer, WET Labs Inc., USA). Based on the LOPC data, abundance (individuals/m**3) and biovolume (mm3/m**3) were calculated as described in the LOPC Software Operation Manual [(Anonymous, 2006), http://www.brooke-ocean.com/index.html]. LOPC data were regrouped into 49 size groups of equal log10(body volume) increments, see Edvardsen et al. (2002, doi:10.3354/meps227205). LOPC data quality was checked as described in Basedow et al. (2013, doi:10.1016/j.pocean.2012.10.005). Fluorescence was roughly converted into chlorophyll based on filtered chlorophyll values obtained from station 10 in the Labrador Sea. Due to the low number of filtered samples that was used for the conversion the resulting chlorophyll values should be considered with care. CTD data were screened for erroneous (out of range) values and then averaged to the same frequency as the LOPC data (2 Hz). All data were processed using especially developed scripts in the python programming language. The LOPC is an optical instrument designed to count and measure particles (0.1 to 30 mm equivalent spherical diameter) in the water column, see Herman et al., (2004, doi:10.1093/plankt/fbh095). The size of particles as equivalent spherical diameter (ESD) was computed as described in the manual (Anonymous, 2006), and in more detail in Checkley et al. (2008, doi:10.4319/lo.2008.53.5_part_2.2123) and Gaardsted et al. (2010, doi:10.1111/j.1365-2419.2010.00558.x).

Multi-textured 3D humanoid reconstruction through passive and active automatic techniques = Reconstrucción de humanoides 3D multi-texturados mediante técnicas pasivas y activas automáticas

The proliferation of video games and other applications of computer graphics in everyday life demands a much easier way to create animatable virtual human characters. Traditionally, this has been the job of highly skilled artists and animators that painstakingly model, rig and animate their avatars, and usually have to tune them for each application and transmission/rendering platform. The emergence of virtual/mixed reality environments also calls for practical and costeffective ways to produce custom models of actual people. The purpose of the present dissertation is bringing 3D human scanning closer to the average user. For this, two different techniques are presented, one passive and one active. The first one is a fully automatic system for generating statically multi-textured avatars of real people captured with several standard cameras. Our system uses a state-of-the-art shape from silhouette technique to retrieve the shape of subject. However, to deal with the lack of detail that is common in the facial region for these kind of techniques, which do not handle concavities correctly, our system proposes an approach to improve the quality of this region. This face enhancement technique uses a generic facial model which is transformed according to the specific facial features of the subject. Moreover, this system features a novel technique for generating view-independent texture atlases computed from the original images. This static multi-texturing system yields a seamless texture atlas calculated by combining the color information from several photos. We suppress the color seams due to image misalignments and irregular lighting conditions that multi-texturing approaches typically suffer from, while minimizing the blurring effect introduced by color blending techniques. The second technique features a system to retrieve a fully animatable 3D model of a human using a commercial depth sensor. Differently to other approaches in the current state of the art, our system does not require the user to be completely still through the scanning process, and neither the depth sensor is moved around the subject to cover all its surface. Instead, the depth sensor remains static and the skeleton tracking information is used to compensate the user’s movements during the scanning stage. RESUMEN La popularización de videojuegos y otras aplicaciones de los gráficos por ordenador en el día a día requiere una manera más sencilla de crear modelos virtuales humanos animables. Tradicionalmente, estos modelos han sido creados por artistas profesionales que cuidadosamente los modelan y animan, y que tienen que adaptar específicamente para cada aplicación y plataforma de transmisión o visualización. La aparición de los entornos de realidad virtual/mixta aumenta incluso más la demanda de técnicas prácticas y baratas para producir modelos 3D representando personas reales. El objetivo de esta tesis es acercar el escaneo de humanos en 3D al usuario medio. Para ello, se presentan dos técnicas diferentes, una pasiva y una activa. La primera es un sistema automático para generar avatares multi-texturizados de personas reales mediante una serie de cámaras comunes. Nuestro sistema usa técnicas del estado del arte basadas en shape from silhouette para extraer la forma del sujeto a escanear. Sin embargo, este tipo de técnicas no gestiona las concavidades correctamente, por lo que nuestro sistema propone una manera de incrementar la calidad en una región del modelo que se ve especialmente afectada: la cara. Esta técnica de mejora facial usa un modelo 3D genérico de una cara y lo modifica según los rasgos faciales específicos del sujeto. Además, el sistema incluye una novedosa técnica para generar un atlas de textura a partir de las imágenes capturadas. Este sistema de multi-texturización consigue un atlas de textura sin transiciones abruptas de color gracias a su manera de mezclar la información de color de varias imágenes sobre cada triángulo. Todas las costuras y discontinuidades de color debidas a las condiciones de iluminación irregulares son eliminadas, minimizando el efecto de desenfoque de la interpolación que normalmente introducen este tipo de métodos. La segunda técnica presenta un sistema para conseguir un modelo humano 3D completamente animable utilizando un sensor de profundidad. A diferencia de otros métodos del estado de arte, nuestro sistema no requiere que el usuario esté completamente quieto durante el proceso de escaneado, ni mover el sensor alrededor del sujeto para cubrir toda su superficie. Por el contrario, el sensor se mantiene estático y el esqueleto virtual de la persona, que se va siguiendo durante el proceso, se utiliza para compensar sus movimientos durante el escaneado.

In situ measurements of hydrogen sulfide, oxygen, and temperature in diffuse fluids of the ultramafic-hosted Logatchev hydrothermal vent field (Mid-Atlantic Ridge)

The Logatchev hydrothermal vent field (14°45'N, Mid-Atlantic Ridge) is located in a ridge segment characterized by mantle-derived ultramafic outcrops. Compared to basalt-hosted vents, Logatchev high temperature fluids are relatively low in sulfide indicating that the diffuse, low temperature fluids of this vent field may not contain sufficient sulfide concentrations to support a chemosymbiotic invertebrate community. However, the high abundances of bathymodiolin mussels with bacterial symbionts related to free-living sulfur oxidizing bacteria suggested that bioavailable sulfide is present at Logatchev. To clarify if diffuse fluids above mussel beds of Bathymodiolus puteoserpentis provide the reductants and oxidants needed by their symbionts for aerobic sulfide oxidation, in situ microsensor measurements of dissolved hydrogen sulfide and oxygen were combined with simultaneous temperature measurements. High temporal fluctuations of all three parameters were measured above the mussel beds. H2S and O2 co-existed with mean concentrations between 9-31 µM (H2S) and 216-228 µM (O2). Temperature maxima (<= 7.4°C) were generally concurrent with H2S maxima (<= 156 µM) and O2 minima (>= 142 µM). Long-term measurements for 250 days using temperature as a proxy for oxygen and sulfide concentrations indicated that the mussels were neither oxygen- nor sulfide-limited. Our in situ measurements at Logatchev indicate that sulfide may also be bioavailable in diffuse fluids from other ultramafic-hosted vents along slow- and ultraslow-spreading ridges.

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

Reconeixement de gestos de la mà amb el sensor Kinect

El reconeixement dels gestos de la mà (HGR, Hand Gesture Recognition) és actualment un camp important de recerca degut a la varietat de situacions en les quals és necessari comunicar-se mitjançant signes, com pot ser la comunicació entre persones que utilitzen la llengua de signes i les que no. En aquest projecte es presenta un mètode de reconeixement de gestos de la mà a temps real utilitzant el sensor Kinect per Microsoft Xbox, implementat en un entorn Linux (Ubuntu) amb llenguatge de programació Python i utilitzant la llibreria de visió artifical OpenCV per a processar les dades sobre un ordinador portàtil convencional. Gràcies a la capacitat del sensor Kinect de capturar dades de profunditat d’una escena es poden determinar les posicions i trajectòries dels objectes en 3 dimensions, el que implica poder realitzar una anàlisi complerta a temps real d’una imatge o d’una seqüencia d’imatges. El procediment de reconeixement que es planteja es basa en la segmentació de la imatge per poder treballar únicament amb la mà, en la detecció dels contorns, per després obtenir l’envolupant convexa i els defectes convexos, que finalment han de servir per determinar el nombre de dits i concloure en la interpretació del gest; el resultat final és la transcripció del seu significat en una finestra que serveix d’interfície amb l’interlocutor. L’aplicació permet reconèixer els números del 0 al 5, ja que s’analitza únicament una mà, alguns gestos populars i algunes de les lletres de l’alfabet dactilològic de la llengua de signes catalana. El projecte és doncs, la porta d’entrada al camp del reconeixement de gestos i la base d’un futur sistema de reconeixement de la llengua de signes capaç de transcriure tant els signes dinàmics com l’alfabet dactilològic.

Reconeixement de gestos de la mà amb el sensor Kinect

El reconeixement dels gestos de la mà (HGR, Hand Gesture Recognition) és actualment un camp important de recerca degut a la varietat de situacions en les quals és necessari comunicar-se mitjançant signes, com pot ser la comunicació entre persones que utilitzen la llengua de signes i les que no. En aquest projecte es presenta un mètode de reconeixement de gestos de la mà a temps real utilitzant el sensor Kinect per Microsoft Xbox, implementat en un entorn Linux (Ubuntu) amb llenguatge de programació Python i utilitzant la llibreria de visió artifical OpenCV per a processar les dades sobre un ordinador portàtil convencional. Gràcies a la capacitat del sensor Kinect de capturar dades de profunditat d’una escena es poden determinar les posicions i trajectòries dels objectes en 3 dimensions, el que implica poder realitzar una anàlisi complerta a temps real d’una imatge o d’una seqüencia d’imatges. El procediment de reconeixement que es planteja es basa en la segmentació de la imatge per poder treballar únicament amb la mà, en la detecció dels contorns, per després obtenir l’envolupant convexa i els defectes convexos, que finalment han de servir per determinar el nombre de dits i concloure en la interpretació del gest; el resultat final és la transcripció del seu significat en una finestra que serveix d’interfície amb l’interlocutor. L’aplicació permet reconèixer els números del 0 al 5, ja que s’analitza únicament una mà, alguns gestos populars i algunes de les lletres de l’alfabet dactilològic de la llengua de signes catalana. El projecte és doncs, la porta d’entrada al camp del reconeixement de gestos i la base d’un futur sistema de reconeixement de la llengua de signes capaç de transcriure tant els signes dinàmics com l’alfabet dactilològic.

On the use of a low-cost thermal sensor to improve kinect people detection in a mobile robot

[EN]Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform.

Sistema de visão robótico baseado no sensor kinect para orientação no espaço e contorno de obstáculos

Dissertação de mestrado em Engenharia Eletrónica Industrial e Computadores (área de especialização em Robótica)

Correcció postural esportiva utilitzant un sensor Kinect

Aquest projecte s’emmarca dins de l’àmbit de la visió per computador, concretament en la utilització de dades de profunditat obtingudes a través d’un emissor i sensor de llum infraroja.El propòsit principal d’aquest projecte és mostrar com adaptar aquestes tecnologies, a l’abast de qualsevol particular, de forma que un usuari durant la pràctica d’una activitat esportiva concreta, rebi informació visual continua dels moviments i gestos incorrectes que està realitzant, en base a uns paràmetres prèviament establerts.L’objectiu d’aquest projecte consisteix en fer una lectura constant en temps real d’una persona practicant una selecció de diverses activitats esportives estàtiques utilitzant un sensor Kinect. A través de les dades obtingudes pel sensor Kinect i utilitzant les llibreries de “skeleton traking” proporcionades per Microsoft s’haurà d’interpretar les dades posturals obtingudes per cada tipus d’esport i indicar visualment i d’una manera intuïtiva els errors que està cometent en temps real, de manera que es vegi clarament a quina part del seu cos realitza un moviment incorrecte per tal de poder corregir-lo ràpidament. El entorn de desenvolupament que s’utilitza per desenvolupar aquesta aplicació es Microsoft Viusal Studio 2010.El llenguatge amb el qual es treballarà sobre Microsoft Visual Studio 2010 és C#

Depth sensors in augmented reality solutions. Literature review

The emergence of depth sensors has made it possible to track – not only monocular cues – but also the actual depth values of the environment. This is especially useful in augmented reality solutions, where the position and orientation (pose) of the observer need to be accurately determined. This allows virtual objects to be installed to the view of the user through, for example, a screen of a tablet or augmented reality glasses (e.g. Google glass, etc.). Although the early 3D sensors have been physically quite large, the size of these sensors is decreasing, and possibly – eventually – a 3D sensor could be embedded – for example – to augmented reality glasses. The wider subject area considered in this review is 3D SLAM methods, which take advantage of the 3D information available by modern RGB-D sensors, such as Microsoft Kinect. Thus the review for SLAM (Simultaneous Localization and Mapping) and 3D tracking in augmented reality is a timely subject. We also try to find out the limitations and possibilities of different tracking methods, and how they should be improved, in order to allow efficient integration of the methods to the augmented reality solutions of the future.