Abstract-An important field of reasearch in computer vision is the 3D analysis and reconstruction of objects and scenes for e. g. position determination, online object recognition or collision prevention. Known techniques thereby mainly rely on measuring devices such as laser scanners, stereo camera systems, or comparable algorithms like structure from motion.The rather new Photonic Mixing Device (PMD) technique is based on the time-of-flight principle and measures full-range distance information in real-time. Unfortunately, PMD-based devices have still limited resolution (e. g. 160 × 120 px) and provide only graylevel information. This paper describes a fast algorithmic approach to combine high resolution RGB images with low resolution PMD distance data, acquired using a binocular camera setup. The approach relies completely on fast parallel Graphics Processing Units (GPUs) ressources. Additionally, we introduce a simple but efficient method to detect insufficient color assignments, which are due to the binocular setup.The resulting combined RGBZ-data not only enhances the visual result, but also represents a basis for advanced data processing in e. g. object recognition.
In this work, a secure wireless sensor network (WSN) for the surveillance, monitoring and protection of critical infrastructures was developed. To guarantee the security of the system, the main focus was the implementation of a unique security concept, which includes both security on the communication level, as well as mechanisms that ensure the functional safety during its operation. While there are many theoretical approaches in various subdomains of WSNs-like network structures, communication protocols and security concepts-the construction, implementation and real-life application of these devices is still rare. This work deals with these aforementioned aspects, including all phases from concept-generation to operation of a secure wireless sensor network. While the key focus of this paper lies on the security and safety features of the WSN, the detection, localization and J. Sens. Actuator Netw. 2015, 4 337 classification capabilities resulting from the interaction of the nodes' different sensor types are also described.
Abstract. For a locally compact group G and a group B of topological automorphisms containing the inner automorphisms of G and being relatively compact with respect to Birkhofftopology (that is G e [FIA]j~, B ~_ I (G)) the space Gz~ of B-orbits is a commutative hypergroup (= commutative convo in JEWETT's terminology) in a natural way as JEWETT has shown. Identifying the space of hypergroup characters of G z with E (G,B) (the extreme points of B-invariant positive definite continuous functions p with p (e) = l, endowed with the topology of compact convergence) we prove that E (G, B) is a hypergroup, the "hypergroup dual" of G z. Introduction The theory of topological hypergroups was initiated by DUXKL 16'.'
Modern time-of-flight measuring camera systems are A further problem with these TOF cameras lies in their able to provide accurate depth images but there are problems measurement principle for the dynamic case, as the need to with regard to their usage in a dynamic environment due to their capture a number of phase images in order to compute a measurement principle. The Centre for Sensor Systems recently distance image leads to mixing phase images with different proposed a new 2D/3D camera avoiding this drawback by the relative positions to a scene and thus to erroneous distances or monocular combination of a 2D and a 3D sensor. In this paper, the camera is presented and an approach for detecting erroneous movement artefacts. In ths paper, this aspect shall be discussed, depth values due to movement is made. and a possible solution based on the combination 2D/3D shall be presented.
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