系统地回顾大数据在旅游研究中的应用,对于理解旅游研究范式的转型,响应新涌现的科学问题和实践应用问题具有重要意义。对Web of Science、Archive和中国知网3个数据库中的2477篇旅游大数据文献进行了综述。研究发现:(1)从2010年开始,...
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系统地回顾大数据在旅游研究中的应用,对于理解旅游研究范式的转型,响应新涌现的科学问题和实践应用问题具有重要意义。对Web of Science、Archive和中国知网3个数据库中的2477篇旅游大数据文献进行了综述。研究发现:(1)从2010年开始,旅游大数据研究文献数量逐年增长。中国研究者发表了838篇旅游大数据研究论文,占文献总量的33.83%。(2)接近50.00%的论文以会议和学位论文的形式发表,超过70.00%的文献发表在非旅游类期刊。(3)62.65%的论文利用了TripAdvisor、携程旅行网、马蜂窝等的UGC数据。(4)预测旅游需求、旅游推荐、旅游消费行为、游客流动模式、旅游目的地形象、游客满意度、景观评价和方法创新是当前研究聚焦的八大场景。(5)目前旅游研究领域对大数据应用方法的创新贡献不足,主要通过迁移数据科学与信息科学等已经发展较为成熟的方法,结合旅游情景和数据开展研究。
In order to improve the quality and solve the problem of low speed of imagereconstruction in the traditional optical computerized tomography (OCT) when the data acquired is incomplete projection, the multiple constra...
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ISBN:
(纸本)9780819470072
In order to improve the quality and solve the problem of low speed of imagereconstruction in the traditional optical computerized tomography (OCT) when the data acquired is incomplete projection, the multiple constrained of genetic algorithm based on algebraic iterative was proposed. Generally speaking, under the condition of multiple-objective optimization, the common extreme point for all the objective functions doesn't exist. So we can achieve the preferable compromise in the contradictions of multiple objectives. In this article, there are three constrained conditions. The first one is the maximum entropy criterion which is used mostly to solve the problem of OCT imagereconstruction when the data acquired is incomplete projection recently. The second one is the minimum criteria of peak value which is introduced to suppress noise effectively and ensure the gliding property of the imagereconstruction, because of the first one leading to noise amplification during the iterative process. The last constrained condition is the minimum criteria of the difference between the projection again of imagereconstruction and the original projection. The concept of penalize-function is introduced into the genetic algorithm, which would transform the constrained optimization problem to unconstrained. It is clearly demonstrated from the experiment results that the algorithm reconstruction technique can efficiently improve the quality of images reconstruction of the incomplete projection data.
BACKGROUND: For sparse and limited angle projection Computed Tomography (CT), the reconstructed image usually suffers from considerable artifacts due to undersampled data. OBJECTIVE: To improve imagereconstruction qu...
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BACKGROUND: For sparse and limited angle projection Computed Tomography (CT), the reconstructed image usually suffers from considerable artifacts due to undersampled data. OBJECTIVE: To improve imagereconstruction quality of sparse and limited angle projection CT, this study tested a novel reconstruction algorithm based on Dictionary Learning (DL) from sparse and limited projections. METHODS: The study used signal sparse representation and feature extraction to render the DL technology, which is constrained by L2 and Lp norms, respectively. A Lp Norm Dictionary Learning term is suitable for regular term of objective function for CT imagereconstruction. This is helpful for solving the objective function by combining algorithm of ART. Based on these features, the new algorithm of ART-DL-Lp is proposed for CT imagereconstruction. The alternate solving strategy of the algorithm of "ART first, then adaptive DL" is provided in sequence. The impact on reconstruction results of ART-DL-Lp at different p values (0 < p < 1) is also considered. RESULTS: For non-ideal projections with noise, the digital experiments show that ART-DL-Lp data were superior to those of ART, SART, and ART-DL-L2. Accordingly, the objective evaluation metrics for non-ideal situation of RMSE, MAE, PSNR, Residuals and SSIM are all better than those of contrasted three algorithms. The metrics curves of ART-DL-Lp algorithm are recorded as the best. In both incomplete projection situations, smaller p-value of ART-DL-Lp algorithm induces more close reconstructed images to the original form and better five objective evaluation metrics. CONCLUSIONS: Overall, the reconstruction efficiency of the proposed ART-DL-Lp for CT imaging using the noisy incomplete projections outperforms ART, SART and ART-DL-L2 algorithms. For ART-DL-Lp algorithm, lower p-values result in better reconstruction performance.
Use of incompleteimagedata has become a prominent research issue in recent years, driven by the development of space variant image sensors. Whilst imagereconstruction techniques have been developed that enable the ...
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ISBN:
(纸本)9781424417650
Use of incompleteimagedata has become a prominent research issue in recent years, driven by the development of space variant image sensors. Whilst imagereconstruction techniques have been developed that enable the subsequent use of standard image processing algorithms, the development of image processing algorithms that can be applied directly to incompleteimagedata has received less attention. The problem of interest point detection for incompleteimages is addressed by presenting an algorithm that can be applied directly to incompleteimagedata without the requirement of imagereconstruction, and the accurate performance of the algorithm is illustrated through visual results and ROC curves.
A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are ...
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ISBN:
(纸本)0819437689
A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectation maximization algorithm and an example based on Cowpea mosaic virus is provided.
Imaging interferometry suffers from sparse Fourier measurements, and, at the visible wavelengths, a lack of phase information, creating a need for an imagereconstruction algorithm. A support constraint is useful for ...
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ISBN:
(纸本)9780819492173
Imaging interferometry suffers from sparse Fourier measurements, and, at the visible wavelengths, a lack of phase information, creating a need for an imagereconstruction algorithm. A support constraint is useful for optimization but is often not known a priori. The two-point rule for finding an object support from the autocorrelation is limited in usefulness by the sparsity and non-uniformity of the Fourier data and is insufficient for imagereconstruction. Compactness, a common prior, does not require knowledge of the support. Compactness penalizes solutions that have bright pixels away from the center, favoring soft-edged objects with a bright center and darker extremities. With regards to imaging hard-edged objects such as satellites, a support constraint is desired but unknown and compactness may be unfavorable. Combining various techniques, a method of simultaneously estimating the object's support and the object's intensity distribution is presented. Though all the optimization parameters are in the image domain, we are effectively performing phase retrieval at the measurement locations and interpolation between the sparse data points.
In this paper, we describe a practical implementation of an imagereconstruction method designed to generate a map of the brightness distribution fromdata consisting of squared visibilities and complex closure amplit...
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ISBN:
(纸本)9780819482969
In this paper, we describe a practical implementation of an imagereconstruction method designed to generate a map of the brightness distribution fromdata consisting of squared visibilities and complex closure amplitudes resulting from observations of an astronomical target with a broadband, multichannel, spatial optical interferometer. Given the data, the method estimates the true brightness distribution with a model sampled on a rectangular grid of discrete positions on the sky with the assumption that the model intensities in the region not defined by the discrete positions being described by bilinear interpolation of the discrete intensities. The developed imagereconstruction method has been applied to real observational data obtained from existing optical interferometer facilities.
Computed tomography (CT) is a widely used imaging technique in both medical and industrial applications. However, accurate CT reconstruction requires complete projection data, while incompletedata can result in signi...
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Computed tomography (CT) is a widely used imaging technique in both medical and industrial applications. However, accurate CT reconstruction requires complete projection data, while incompletedata can result in significant artifacts in the reconstructed images, compromising their reliability for subsequent detection and diagnosis. As a result, accurate CT reconstructionfromincomplete projection data remains a challenging research area in radiology. With the rapid development of deep learning (DL) techniques, many DL-based methods have been proposed for CT reconstructionfromincomplete projection data. However, there are limited comprehensive surveys that summarize recent advances in this field. This article provides a comprehensive overview of the current state-of-the-art DL-based CT reconstructionfromincomplete projection data, including acrlong SV reconstruction, acrlong LA reconstruction, acrlong MAR, acrlong IT, and ring artifact reduction. This survey covers various DL-based solutions to the five problems, potential limitations of existing methods, and future research directions.
Measuring a series of far-field intensity patterns from an object, taken after a, transverse translation of the object with respect to a known illumination pattern, has been shown to make the problem of image reconstr...
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ISBN:
(纸本)9780819472960
Measuring a series of far-field intensity patterns from an object, taken after a, transverse translation of the object with respect to a known illumination pattern, has been shown to make the problem of imagereconstruction by phase retrieval much more robust. However, previously reported reconstruction algorithms [Phys. Rev. Lett. 93, 023903 (2004)] rely oil an accurate knowledge of the translations and illumination pattern for a successful reconstruction. We developed a nonlinear optimization algorithm that allows optimization over the translations and illumination pattern, dramatically improving the reconstructions if the system parameters are inaccurately known [Opt. Express 16, 7264 (2008)]. In this paper we compare reconstructions obtained with these algorithms under realistic experimental scenarios.
A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described. The method is based on a Gaussian mi...
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ISBN:
(纸本)9780819482969
A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described. The method is based on a Gaussian mixture description of the heterogeneity and is motivated by cryo electron microscopy of biological objects.
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