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Abstract In the heavy construction industry, pipe spool assembly is geometrically complex and suboptimal fabrication processes inevitably lead to fabrication errors and costly rework. In an attempt to mitigate fabrication risks and improve product quality, computer aided tools are being developed to provide an additional layer of control. In this paper, the data acquisition capabilities of two low-cost range cameras are investigated for the eventual purpose of pipe fitting process monitoring in a smart fabrication facility environment. Range images of various pipes are systematically taken at varying distances from the sensor. By using the proposed radius estimation algorithm, the utility of the data for accurate geometrical pipe feature detection is evaluated by a radius feature metric. Results show that the algorithm reliably extracts radius information from point clouds representing piping. In conjunction with the algorithm, the low-cost range camera hardware was able to characterize pipes, of radius ranging from 2.41 cm to 8.78 cm at a distance from the sensor ranging from 0.5 m to 3.75 m, with an average error of 18% for Kinect 1 and 10% for Kinect 2. Highlights • Low-cost range cameras are investigated for 3D geometric modeling. • A statistical approach is performed for radius detection and estimation from 3D point clouds. • Optimal nearest neighborhood size for various devices are analyzed. • Range cameras are capable of detecting and estimating cylindrical objects at close ranges. [ABSTRACT FROM AUTHOR]

3D video applications are easily accessible to consumers in this era. In general, high quality depth maps are crucial for interpolating virtual view images that are vital in 3D video applications. Many applications adopt time-of-flight (ToF) cameras due to their real-time distance capturing. Yet, the raw ToF image exhibit limitations such as inaccurate distance values in object boundaries and areas that can absorb the infrared ray. This paper presents a method that handles this issue. The existing methods do not focus on this problem. The proposed method is tested on a camera system that consists of a color camera and a ToF camera. Assuming immersive applications such as teleconferencing or virtual broadcasting, the object of interest is captured. The errors existing in the ToF images are modified as a preprocessing step. Afterward, the low-resolution ToF image is warped to the viewpoint of the color camera; empty areas are filled considering the neighbor information. The depth map results show improvements over the state-of-the-art method. The proposed method can increase the overall quality of the 3D video system, ultimately making 3D video consumer applications more marketable. [ABSTRACT FROM PUBLISHER]

Due to the large biases between the laboratory-calibrated values of the orientation parameters and their in-orbit true values, the initial direct georeferencing accuracy of the Ziyuan-3 (ZY-3) three-line camera (TLC) images can only reach the kilometre level. In this paper, a point-based geometric calibration model of the ZY-3 TLCs is firstly established by using the collinearity constraint, and then a line-based geometric calibration model is established by using the coplanarity constraint. With the help of both the point-based and the line-based models, a feasible in-orbit geometric calibration approach for the ZY-3 TLCs combining ground control points (GCPs) and ground control lines (GCLs) is presented. Experimental results show that like GCPs, GCLs can also provide effective ground control information for the geometric calibration of the ZY-3 TLCs. The calibration accuracy of the look angles of charge-coupled device (CCD) detectors achieved by using the presented approach reached up to about 1.0''. After the geometric calibration, the direct georeferencing accuracy of the ZY-3 TLC images without ground controls was significantly improved from the kilometre level to better than 11 m in planimetry and 9 m in height. A more satisfactory georeferencing accuracy of better than 3.5 m in planimetry and 3.0 m in height was achieved after the block adjustment with four GCPs. [ABSTRACT FROM AUTHOR]

To efficiently locate identical objects in heterogeneous cameras and possibly propagate reliable information between cameras and refine detection, many techniques were used to recognize vehicles. In this paper, we investigate several key problems and present a novel approach for automatic vehicle recognition (AVR) in multiple cameras for video surveillance application. We propose a level-based region comparison algorithm to AVR in multiple cameras. For improving the recognition accuracy, new license plate recognition method is also proposed. Experimental results show that the proposed algorithm is simple and efficient, and the quality of the composed image can be comparable with the results of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We identify and study two types of 'accidental' images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are 'inverse' pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene. [ABSTRACT FROM AUTHOR]

Sensors of various modalities and capabilities, especially cameras, have become ubiquitous in our environment. Their intended use is wide ranging and encompasses surveillance, transportation, entertainment, education, healthcare, emergency response, disaster recovery, and the like. Technological advances and the low cost of such sensors enable deployment of large-scale camera networks in large metropolises such as London and New York. Multimedia algorithms for analyzing and drawing inferences from video and audio have also matured tremendously in recent times. Despite all these advances, large-scale reliable systems for media-rich sensor-based applications, often classified as situation-awareness applications, are yet to become commonplace. Why is that? There are several forces at work here. First, the system abstractions are just not at the right level for quickly prototyping such applications on a large scale. Second, while Moore's law has held true for predicting the growth of processing power, the volume of data that applications are called upon to handle is growing similarly, if not faster. Enormous amount of sensing data is continually generated for real-time analysis in such applications. Further, due to the very nature of the application domain, there are dynamic and demanding resource requirements for such analyses. The lack of right set of abstractions for programing such applications coupled with their data-intensive nature have hitherto made realizing reliable large-scale situation-awareness applications difficult. Incidentally, situation awareness is a very popular but ill-defined research area that has attracted researchers from many different fields. In this paper, we adopt a strong systems perspective and consider the components that are essential in realizing a fully functional situation-awareness system. [ABSTRACT FROM AUTHOR]

A combined IR and visible camera system [G. A.Wurden et al., "A high resolution IR/visible imaging system for the W7-X limiter," Rev. Sci. Instrum. (these proceedings)] and a filterscope system [R. J. Colchin et al., Rev. Sci. Instrum. 74, 2068 (2003)] were implemented together to obtain spectroscopic data of limiter and first wall recycling and impurity sources during Wendelstein 7-X startup plasmas. Both systems together provided excellent temporal and spatial spectroscopic resolution of limiter 3. Narrowband interference filters in front of the camera yielded C-III and Hα photon flux, and the filterscope system provided Hα, Hβ, He-I, He-II, C-II, and visible bremsstrahlung data. The filterscopes made additional measurements of several points on the W7-X vacuum vessel to yield wall recycling fluxes. The resulting photon flux from both the visible camera and filterscopes can then be compared to an EMC3-EIRENE synthetic diagnostic [H. Frerichs et al., "Synthetic plasma edge diagnostics for EMC3-EIRENE, highlighted for Wendelstein 7-X," Rev. Sci. Instrum. (these proceedings)] to infer both a limiter particle flux and wall particle flux, both of which will ultimately be used to infer the complete particle balance and particle confinement time τP. [ABSTRACT FROM AUTHOR]