Motion capture based on RGBD data from multiple sensors for avatar animation

With recent advances in technology and emergence of affordable RGB-D sensors for a wider range of users, markerless motion capture has become an active field of research both in computer vision and computer graphics. In this thesis, we designed a POC (Proof of Concept) for a new tool that enables us to perform motion capture by using a variable number of commodity RGB-D sensors of different brands and technical specifications on constraint-less layout environments. The main goal of this work is to provide a tool with motion capture capabilities by using a handful of RGB-D sensors, without imposing strong requirements in terms of lighting, background or extension of the motion capture area. Of course, the number of RGB-D sensors needed is inversely proportional to their resolution, and directly proportional to the size of the area to track to. Built on top of the OpenNI 2 library, we made this POC compatible with most of the nonhigh-end RGB-D sensors currently available in the market. Due to the lack of resources on a single computer, in order to support more than a couple of sensors working simultaneously, we need a setup composed of multiple computers. In order to keep data coherency and synchronization across sensors and computers, our tool makes use of a semi-automatic calibration method and a message-oriented network protocol. From color and depth data given by a sensor, we can also obtain a 3D pointcloud representation of the environment. By combining pointclouds from multiple sensors, we can collect a complete and animated 3D pointcloud that can be visualized from any viewpoint. Given a 3D avatar model and its corresponding attached skeleton, we can use an iterative optimization method (e.g. Simplex) to find a fit between each pointcloud frame and a skeleton configuration, resulting in 3D avatar animation when using such skeleton configurations as key frames

Sistema d'interacció basat en Kinect per a realitat virtual

Aquest projecte se centra en l'ús de la càmera Microsoft Kinect per tal de construir una aplicació que utilitzi les dades proporcionades per la càmera i permeti d'una manera senzilla construir interfícies per interactuar amb un entorn de realitat virtual

A multi-projector CAVE system with commodity hardware and gesture-based interaction

Spatially-immersive systems such as CAVEs provide users with surrounding worlds by projecting 3D models on multiple screens around the viewer. Compared to alternative immersive systems such as HMDs, CAVE systems are a powerful tool for collaborative inspection of virtual environments due to better use of peripheral vision, less sensitivity to tracking errors, and higher communication possibilities among users. Unfortunately, traditional CAVE setups require sophisticated equipment including stereo-ready projectors and tracking systems with high acquisition and maintenance costs. In this paper we present the design and construction of a passive-stereo, four-wall CAVE system based on commodity hardware. Our system works with any mix of a wide range of projector models that can be replaced independently at any time, and achieves high resolution and brightness at a minimum cost. The key ingredients of our CAVE are a self-calibration approach that guarantees continuity across the screen, as well as a gesture-based interaction approach based on a clever combination of skeletal data from multiple Kinect sensors.Preprin

Choosing the right cell line for rectal cancer research

Up to date no effective method exists that predicts response to preoperative chemoradiation (CRT) in locally advanced rectal cancer (LARC). Nevertheless, identification of patients who have a higher likelihood of responding to preoperative CRT could be crucial in decreasing treatment morbidity and avoiding expensive and time-consuming treatments. Using the Gng4, c-Myc, Pola1, and Rrm1 signature, we were able to establish a model to predict response to CRT in rectal cancer with a sensitivity of 60% and 100% specificity. The aim of this study was to characterize c-Myc status in DNA, RNA and protein levels in 3 tumoral cell lines (SW480, SW620 and SW837) to establish the best cell line model and, subsequently, carry out genome silencing of c-Myc by means of RNA interference (iRNA). To study the expression levels of c-Myc, we used Polymerase Chain Reaction (PCR) amplifications and sequencing; quantitative real time PCR (qRT-PCR); and western blot analysis in each cell line. SW480 and SW620 showed a variation A > G in exon 2, which caused a substitution of aspargine to serine, and SW837 revealed a G > A transition in the same, which caused a mutation at codon 92. The three cell lines expressed c-Myc mRNA. SW837 showed a decrease of c-Myc expression levels compared with SW480, and SW620. At protein level, SW620 showed the highest expression of c-Myc. According to the results obtained, we can perform c-Myc gene silencing experiments to analyze the role of this biomarker in response to treatment

Sistema d'interacció basat en Kinect per a realitat virtual

Aquest projecte se centra en l'ús de la càmera Microsoft Kinect per tal de construir una aplicació que utilitzi les dades proporcionades per la càmera i permeti d'una manera senzilla construir interfícies per interactuar amb un entorn de realitat virtual

Sistema d'interacció basat en Kinect per a realitat virtual

Aquest projecte se centra en l'ús de la càmera Microsoft Kinect per tal de construir una aplicació que utilitzi les dades proporcionades per la càmera i permeti d'una manera senzilla construir interfícies per interactuar amb un entorn de realitat virtual

Motion capture based on RGBD data from multiple sensors for avatar animation

With recent advances in technology and emergence of affordable RGB-D sensors for a wider range of users, markerless motion capture has become an active field of research both in computer vision and computer graphics. In this thesis, we designed a POC (Proof of Concept) for a new tool that enables us to perform motion capture by using a variable number of commodity RGB-D sensors of different brands and technical specifications on constraint-less layout environments. The main goal of this work is to provide a tool with motion capture capabilities by using a handful of RGB-D sensors, without imposing strong requirements in terms of lighting, background or extension of the motion capture area. Of course, the number of RGB-D sensors needed is inversely proportional to their resolution, and directly proportional to the size of the area to track to. Built on top of the OpenNI 2 library, we made this POC compatible with most of the nonhigh-end RGB-D sensors currently available in the market. Due to the lack of resources on a single computer, in order to support more than a couple of sensors working simultaneously, we need a setup composed of multiple computers. In order to keep data coherency and synchronization across sensors and computers, our tool makes use of a semi-automatic calibration method and a message-oriented network protocol. From color and depth data given by a sensor, we can also obtain a 3D pointcloud representation of the environment. By combining pointclouds from multiple sensors, we can collect a complete and animated 3D pointcloud that can be visualized from any viewpoint. Given a 3D avatar model and its corresponding attached skeleton, we can use an iterative optimization method (e.g. Simplex) to find a fit between each pointcloud frame and a skeleton configuration, resulting in 3D avatar animation when using such skeleton configurations as key frames

Posttranslational processing of GTP-binding proteins

SIGLEAvailable from British Library Document Supply Centre- DSC:DX171922 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A multi-projector CAVE system with commodity hardware and gesture-based interaction

Spatially-immersive systems such as CAVEs provide users with surrounding worlds by projecting 3D models on multiple screens around the viewer. Compared to alternative immersive systems such as HMDs, CAVE systems are a powerful tool for collaborative inspection of virtual environments due to better use of peripheral vision, less sensitivity to tracking errors, and higher communication possibilities among users. Unfortunately, traditional CAVE setups require sophisticated equipment including stereo-ready projectors and tracking systems with high acquisition and maintenance costs. In this paper we present the design and construction of a passive-stereo, four-wall CAVE system based on commodity hardware. Our system works with any mix of a wide range of projector models that can be replaced independently at any time, and achieves high resolution and brightness at a minimum cost. The key ingredients of our CAVE are a self-calibration approach that guarantees continuity across the screen, as well as a gesture-based interaction approach based on a clever combination of skeletal data from multiple Kinect sensors