Mission success is highly dependent on the ability to accomplish Surveillance, Situation Awareness, Target Detection and Classification, but is challenging under adverse weather conditions. This paper introduces an en...
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ISBN:
(纸本)9780819471376
Mission success is highly dependent on the ability to accomplish Surveillance, Situation Awareness, Target Detection and Classification, but is challenging under adverse weather conditions. This paper introduces an engineering prototype to address the image collection challenges using a Common Optical Path, Multiple Sensors and an Intelligent Image fusion System, and provides illustrations and sample fusion images. Panavision's advanced wide spectrum optical design has permitted a suite of imagers to perform observations through a common optical path with a common field of view, thereby aligning images and facilitating optimized downstream image processing. The adaptable design also supports continuous zoom or Galilean lenses for multiple field of views. The Multiple Sensors include: (1) High-definition imaging sensors that are small, have low power consumption and a wide dynamic range;(2) EMCCD sensors that transition from daylight to starlight, even under poor weather conditions, with sensitivity down to 0.00025 Lux;and (3) SWIR sensors that, with the advancement in InGaAs, are able to generate ultra-high sensitivity images from 1-1.7 mu m reflective light and can achieve imaging through haze and some types of camouflage. The intelligent fusion of multiple sensors provides high-resolution color information with previously impossible sensitivity and contrast. With the integration of Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), real-time Image Processing and fusion algorithms can facilitate mission success in a small, low power package.
Over the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integ...
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Over the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integrating them into a consistent, accurate and intelligible data set. Meanwhile, since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it has been successfully applied to structural damage detection (Jiang et al. 2004;Klenke et al. 1996;Ko et al. 1999;Ni et al. 2001). In order to make full use of multi-sensor data (or information) from multi-resource and to improve the diagnosis accuracy of the health conditions for complex structures, it is advisable to combine these methods and exploit their individual advantages. In this paper, a 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented. The proposed method is then applied to damage detection and identification of two simulation examples. The result shows that the proposed method is feasible and effective for damage identification.
Combining the multiscale capability from wavelet with the performance of real-time and recursion about Kalman filter, a multiscale sequential filter is proposed to process dynamic systems with multisensor. This filter...
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ISBN:
(纸本)0780393953
Combining the multiscale capability from wavelet with the performance of real-time and recursion about Kalman filter, a multiscale sequential filter is proposed to process dynamic systems with multisensor. This filter can not only absolutely achieve the effect obtained via conventional multisensor fusion approach, but also it has the advantages as wavelet and Kalman filter. Its multiscale characteristic can be used to analyze stochastic signal in different frequency subspace. Some similar methods existed do not possess these capabilities, such as real time and recursion. Computer simulation also shows that all estimate results from the new algorithm is comparable with that from traditional date fusion algorithms. Finally, the computable advantage is likewise validated by comparing the computer burden between the new algorithm and other two existed fusion algorithms.
We propose a new algorithm to determine the multifocus image fusion from several polychromatic images captured from the best focusing region where the best in focus image is included from a biological sample. This foc...
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We propose a new algorithm to determine the multifocus image fusion from several polychromatic images captured from the best focusing region where the best in focus image is included from a biological sample. This focusing region is built by including several images up and down starting from the Z position of the best image in focus. These captured RGB images are converted to YCbCr color space to have the color CbCr and intensity Y channels separated with the objective to preserve the color information of the best in focus image. Several approaches have been developed to fuse images, like those algorithms based on the wavelets transform, Laplacian, ratio, contrast or morphological pyramids selection, fusion by averaging, Bayesian methods, fuzzy sets, and artificial networks. However, this algorithm utilizes the Fourier approach by using the Y channel frequency content via analyzing the Fourier coefficients to retrieve the high frequencies to obtain the best possible characteristics of every captured image. After the completion of this process, we continue to construct the fused image with these coefficients and color information for the optimum in focus image in the YCbCr color space;as a result, we obtain a precise final RGB fused image. (c) 2005 Society of Photo-Optical Instrumentation Engineers.
Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advan...
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Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion and integration as well as fusion techniques at different fusion levels. Applications of multisensor fusion in robotics, biomedical system, equipment monitoring, remote sensing, and transportation system are also discussed. Finally, future research directions of multisensor fusion technology including microsensors, smart sensors, and adaptive fusion techniques are presented.
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