Currently, VR and AR headsets are becoming widespread. In addition to entertainment purposes, these technologies are increasingly being used in education, science, medicine, and engineering. The remote maintenance mon...
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ISBN:
(纸本)9781510651494;9781510651487
Currently, VR and AR headsets are becoming widespread. In addition to entertainment purposes, these technologies are increasingly being used in education, science, medicine, and engineering. The remote maintenance monitoring technologies make it possible to significantly expand the possibilities of using services for remote maintenance and repair complex technical systems by highly qualified specialists. However, the problems of implementing such systems in a wide range of tasks are complicated by the presence of a wide variety of solutions of this kind and the high price of such models. In this paper, we investigate a new smartphone-based augmented reality device for industrial tasks. The article describes augmented reality glasses based on a mobile phone (system "DAR"), which combines the functions of VR and AR technologies and a low cost of the final product. The proposed solution combines a helmet with a smartphone, which transmits information about the surrounding space and connects the augmented reality elements built on this image. Information about the surrounding space comes to the smartphone screen from stereo cameras equipped with autofocus. images captured in such a system suffer from low contrast and faint color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. This solution makes it possible to expand AR technology scope for remote maintenance of complex technical systems by highly qualified specialists at remote sites since using a smartphone and a DAR headset will be sufficient. Some experimental results are presented to illustrate the performance of the proposed algorithm on the real and synthesized image datasets.
The limited angle problem is a well-known problem in computed tomography. It is caused by missing data over a certain angle interval, which make an inverse Radon transform impossible. In daily routine this problem can...
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ISBN:
(纸本)9780819480231
The limited angle problem is a well-known problem in computed tomography. It is caused by missing data over a certain angle interval, which make an inverse Radon transform impossible. In daily routine this problem can arise for example in tomosynthesis, C-arm CT or dental CT. In the last years there has been a big development in the field of compressed sensing algorithms in computed tomography, which deal very good with incomplete data. The most popular way is to integrate a minimal total variation norm in form of a cost function into the iteration process. To find an exact solution of such a constrained minimization problem, computationally very demanding higher order algorithms should be used. Due to the non perfect sparsity of the total variation representation, reconstructions often show the so called staircase effect. The method proposed here uses the solutions of the iteration process as an estimation for the missing angle data. Compared to a pure compressed sensing-based algorithm we reached much better results within the same number of iterations and could eliminate the staircase effect. The algorithm is evaluated using measured clinical datasets.
The stereo correspondence problem is still a highly active topic of research with many applications in the robotic domain. Still many state of the art algorithms proposed to date are unable to reasonably handle high r...
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Dynamic neural networks are able to alter the topology of the underlying graph in order to provide better performance. A lot of interesting algorithms for topology learning have been published, but no formal descripti...
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This paper describes a novel real-time image and signal processing network, RONIN (TM), which facilitates the rapid design and deployment of systems providing advanced geospatial surveillance and situational awareness...
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ISBN:
(纸本)9780819486271
This paper describes a novel real-time image and signal processing network, RONIN (TM), which facilitates the rapid design and deployment of systems providing advanced geospatial surveillance and situational awareness capability. RONIN (TM) is a distributed software architecture consisting of multiple agents or nodes, which can be configured to implement a variety of state-of-the-art computer vision and signal processingalgorithms. The nodes operate in an asynchronous fashion and can run on a variety of hardware platforms, thus providing a great deal of scalability and flexibility. Complex algorithmic configuration chains can be assembled using an intuitive graphical interface in a plug-and-play manner. RONIN (TM) has been successfully exploited for a number of applications, ranging from remote event detection to complex multiple-camera real-time 3D object reconstruction. This paper describes the motivation behind the creation of the network, the core design features, and presents details of an example application. Finally, the on-going development of the network is discussed, which is focussed on dynamic network reconfiguration. This allows to the network to automatically adapt itself to node or communications failure by intelligently re-routing network communications and through adaptive resource management.
This paper introduces an automatic flood mapping application that is hosted on the Grid processing on Demand (G-POD) Fast Access to imagery (Faire) environment of the European Space Agency. The main objective of the o...
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ISBN:
(纸本)9780819492784
This paper introduces an automatic flood mapping application that is hosted on the Grid processing on Demand (G-POD) Fast Access to imagery (Faire) environment of the European Space Agency. The main objective of the online application is to deliver operationally flooded areas using both recent and historical acquisitions of SAR data. Having as a short-term target the flooding-related exploitation of data generated by the upcoming ESA SENTINEL-1 SAR mission, the flood mapping application consists of two building blocks: i) a set of query tools for selecting the "crisis image" and the optimal corresponding "reference image" from the G-POD archive and ii) an algorithm for extracting flooded areas via change detection using the previously selected "crisis image" and "reference image". Stakeholders in flood management and service providers are able to log onto the flood mapping application to get support for the retrieval, from the rolling archive, of the most appropriate reference image. Potential users will also be able to apply the implemented flood delineation algorithm. The latter combines histogram thresholding, region growing and change detection as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. Both algorithms are computationally efficient and operate with minimum data requirements. The case study of the high magnitude flooding event that occurred in July 2007 on the Severn River, UK, and that was observed with a moderate-resolution SAR sensor as well as airborne photography highlights the performance of the proposed online application. The flood mapping application on G-POD can be used sporadically, i. e. whenever a major flood event occurs and there is a demand for SAR-based flood extent maps. In the long term, a potential extension of the application could consist in systematically extracting flooded areas from all SAR images acquired on a daily, weekly or monthly basis.
Stereo matching plays a significant role in various vision based systems including 3D reconstruction, robot localization, mapping and navigation and it has been an intense area of research for many years. In the last ...
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The "German Traffic Sign Recognition Benchmark" is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and...
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ISBN:
(纸本)9781424496365
The "German Traffic Sign Recognition Benchmark" is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in imageprocessing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.
image enhancement based on Beta function is a widely used method for it is able to fit multiple transformation curves, which is a significant step for image analysis. The key step for the method is to find the appropr...
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ISBN:
(纸本)9781728140681
image enhancement based on Beta function is a widely used method for it is able to fit multiple transformation curves, which is a significant step for image analysis. The key step for the method is to find the appropriate parameters to determine the grayscale transformation function. However, it needs a lot of time to seek applicable parameters when enumeration is used and random optimization algorithms often have failures within a limited time and are prone to fall into the local optimum. In order to solve the problems a serial coupled mode of stochastic optimization algorithms is investigated in the paper. According to the model, the differential evolution algorithm and cuckoo search algorithm are tried in image enhancement through serial coupling mode and compared with the traditional optimization algorithm. The experimental results reveals that the proposed approach is feasible and the performance is more balanced, which has a good performance on the image enhancement.
imagery from unmanned aerial systems (UAS) needs compression prior to transmission to a receiver for further processing. Once received, automated image exploitation algorithms, such as frame-to-frame registration, tar...
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ISBN:
(纸本)9781628415803
imagery from unmanned aerial systems (UAS) needs compression prior to transmission to a receiver for further processing. Once received, automated image exploitation algorithms, such as frame-to-frame registration, target tracking, and target identification, are performed to extract actionable information from the data. Unfortunately, in a compress-then-analyze system, exploitation algorithms must contend with artifacts introduced by lossy compression and transmission. Identifying metrics that enable compression engines to predict exploitation degradation could allow encoders the ability of tailoring compression for specific exploitation algorithms. This study investigates the impact of H.264 and JPEG2000 compression on target tracking through the use of a multi-hypothesis blob tracker. Used quality metrics include PSNR, VIF, and IW-SSIM.
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