SR-4000 and CamCube3.0 Time of Flight (ToF) Cameras: Tests and Comparison

In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the CamCube3.0 by PMD Technologies have been evaluated since they have good performances and are well known to researchers dealing with Time-of- Flight (ToF) cameras. After a brief overview of commercial ToF cameras available on the market and the main specifications of the tested devices, two topics are presented in this paper. First, the influence of camera warm-up on distance measurement is analyzed: a warm-up of 40 minutes is suggested to obtain the measurement stability, especially in the case of the CamCube3.0 camera, that exhibits distance measurement variations of several centimeters. Secondly, the variation of distance measurement precision variation over integration time is presented: distance measurement precisions of some millimeters are obtained in both cases. Finally, a comparison between the two cameras based on the experiments and some information about future work on evaluation of sunlight influence on distance measurements are reporte

Integration between calibrated time-of-flight camera data and multi-image matching approach for architectural survey

In this work, the integration between data provided by Time-of-Flight cameras and a multi-image matching technique for metric surveys of architectural elements is presented. The main advantage is given by the quickness in the data acquisition (few minutes) and the reduced cost of the instruments. The goal of this approach is the automatic extraction of the object breaklines in a 3D environment using a photogrammetric process, which is helpful for the final user exigencies for the reduction of the time needed for the drawing production. The results of the performed tests on some architectural elements will be reported in this paper

Background Subtraction for Time of Flight Imaging

A time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in particular support vector machines. A brief review of one class support vector machines is first given. A method that combines the range and intensity data in two operational modes is then provided. Finally, experimental results are presented and discussed.Facultad de Informátic

Supresión de segundo plano en imágenes de tiempo de vuelo

En este artículo se presenta un método computacional para detectar y extraer el plano de fondo, segundo plano, a partir de datos obtenidos por cámaras de tiempo de vuelo. Se utiliza una variante de un método de clasificación basado en máquinas de soporte vectorial. Considerando las características particulares del tipo de cámaras utilizadas, se incorpora adecuadamente la información de rango e intensidad, y se utiliza la capacidad para obtener secuencias rápidas de datos en una modalidad particular. El artículo revisa las técnicas específicas de reconocimiento de patrones utilizadas, presenta la solución propuesta y muestra resultados experimentales preliminares del método propuesto.VII Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR).Red de Universidades con Carreras en Informática (RedUNCI

Supresión de segundo plano en imágenes de tiempo de vuelo

En este artículo se presenta un método computacional para detectar y extraer el plano de fondo, segundo plano, a partir de datos obtenidos por cámaras de tiempo de vuelo. Se utiliza una variante de un método de clasificación basado en máquinas de soporte vectorial. Considerando las características particulares del tipo de cámaras utilizadas, se incorpora adecuadamente la información de rango e intensidad, y se utiliza la capacidad para obtener secuencias rápidas de datos en una modalidad particular. El artículo revisa las técnicas específicas de reconocimiento de patrones utilizadas, presenta la solución propuesta y muestra resultados experimentales preliminares del método propuesto.VII Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR).Red de Universidades con Carreras en Informática (RedUNCI

Precise Depth Image Based Real-Time 3D Difference Detection

3D difference detection is the task to verify whether the 3D geometry of a real object exactly corresponds to a 3D model of this object. This thesis introduces real-time 3D difference detection with a hand-held depth camera. In contrast to previous works, with the proposed approach, geometric differences can be detected in real time and from arbitrary viewpoints. Therefore, the scan position of the 3D difference detection be changed on the fly, during the 3D scan. Thus, the user can move the scan position closer to the object to inspect details or to bypass occlusions. The main research questions addressed by this thesis are: Q1: How can 3D differences be detected in real time and from arbitrary viewpoints using a single depth camera? Q2: Extending the first question, how can 3D differences be detected with a high precision? Q3: Which accuracy can be achieved with concrete setups of the proposed concept for real time, depth image based 3D difference detection? This thesis answers Q1 by introducing a real-time approach for depth image based 3D difference detection. The real-time difference detection is based on an algorithm which maps the 3D measurements of a depth camera onto an arbitrary 3D model in real time by fusing computer vision (depth imaging and pose estimation) with a computer graphics based analysis-by-synthesis approach. Then, this thesis answers Q2 by providing solutions for enhancing the 3D difference detection accuracy, both by precise pose estimation and by reducing depth measurement noise. A precise variant of the 3D difference detection concept is proposed, which combines two main aspects. First, the precision of the depth camera’s pose estimation is improved by coupling the depth camera with a very precise coordinate measuring machine. Second, measurement noise of the captured depth images is reduced and missing depth information is filled in by extending the 3D difference detection with 3D reconstruction. The accuracy of the proposed 3D difference detection is quantified by a quantitative evaluation. This provides an anwer to Q3. The accuracy is evaluated both for the basic setup and for the variants that focus on a high precision. The quantitative evaluation using real-world data covers both the accuracy which can be achieved with a time-of-flight camera (SwissRanger 4000) and with a structured light depth camera (Kinect). With the basic setup and the structured light depth camera, differences of 8 to 24 millimeters can be detected from one meter measurement distance. With the enhancements proposed for precise 3D difference detection, differences of 4 to 12 millimeters can be detected from one meter measurement distance using the same depth camera. By solving the challenges described by the three research question, this thesis provides a solution for precise real-time 3D difference detection based on depth images. With the approach proposed in this thesis, dense 3D differences can be detected in real time and from arbitrary viewpoints using a single depth camera. Furthermore, by coupling the depth camera with a coordinate measuring machine and by integrating 3D reconstruction in the 3D difference detection, 3D differences can be detected in real time and with a high precision

Hydroelastic Response of Surface-Effect Ship Bow Seals: Large-Scale Experiments and Post-Buckling Analysis.

Bow seals are critical components on advanced marine vehicles that rely on aerostatic support to reduce drag. They consist of a series of open-ended fabric cylinders ("fingers") that contact the free surface and, when inflated, form a compliant pressure barrier. Bow seals are unique in that, unlike a majority of structures in civil and mechanical engineering, bow seals operate in a buckled state. The response characteristics of these structures are of practical interest due to unacceptable wear rates on seal components and difficulties in predicting seal performance. Despite this, the hydroelastic response of the seal system, particularly basic information on seal vibration modes and the mechanisms responsible for seal wear, remains largely unknown. Similarly, estimates of the hydrodynamic loads on the seal system are inaccurate and based on heuristic scaling of data from small-scale experiments, where similitude is challenging to maintain. Thus, a large-scale test system is necessary to obtain accurate estimates of bow seal response. The work is comprised of three parts. Part one presents detailed observations of bow seal response acquired using a large-scale test platform developed as part of the present study. These high-resolution observations, the first of their kind, show bow seal response to be characterized by complex post-buckling behavior. Part two proposes an analytical framework for interpreting the wide range of behavior observed at large scale. Using this framework, key parameters driving seal conformation and stability are identified. It is found that, due to their buckled state, bow seals are highly susceptible to a mode switching instability, which may be a potential mechanism responsible for the damaging vibrations. In part three, a benchtop experiment is used to demonstrate that the scalings identified in this study hold across a wide range of bending rigidities. This work has implications for improving drag and wear characteristics in future bow seal designs. In addition, the scaling parameters identified in this study may govern buckling in other physical systems, such as ice sheets and biological membranes.PhDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107098/1/awiggs_1.pd