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Environmental images that are captured by satellites can provide significant information for weather forecasting, climate warning, and so on. This article introduces a novel deep neural network that integrates a convolutional attention feature extractor (CAFE) in a recurrent neural network frame and a multivariate dynamic time warping (MDTW) loss. The CAFE module is designed to capture the complicated and hidden dependencies within image series between the source variable and the target variable. The proposed method can reconstruct the image series across environmental variables. The performance of the proposed method is validated by experiments using a real-world remote sensing dataset and compared with several representative methods. Experimental results demonstrate the emerging performance of the proposed method for cross-variable image series reconstruction. [ABSTRACT FROM AUTHOR]

This narrative review article was conducted to lay out a summarized but exhaustive review of current literature over mucocutaneous manifestations in 4 dimensions of SARS‐CoV‐2 pandemic: virus itself, treatment‐related, vaccine‐induced, and alteration of chronic dermatologic diseases following infection. Virus and vaccine‐related were mainly self‐limited and non‐severe. Treatment‐related reactions could be life‐threatening. [ABSTRACT FROM AUTHOR]

Abstract: To ensure the delineated boundaries of a series of 2-D images closely following the visually perceivable edges with high boundary coherence between consecutive slices, a cell-based two-region competition algorithm based on a maximum a posteriori (MAP) framework is proposed. It deforms the region boundary in a cell-by-cell fashion through a cell-based two-region competition process. The cell-based deformation is guided by a cell-based MAP framework with a posterior function characterizing the distribution of the cell means in each region, the salience and shape complexity of the region boundary and the boundary coherence of the consecutive slices. The proposed algorithm has been validated using 10 series of breast sonograms, including seven compression series and three freehand series. The compression series contains two carcinoma and five fibroadenoma cases and the freehand series contains two carcinoma and one fibroadenoma cases. The results show that >70% of the derived boundaries fall within the span of the manually delineated boundaries. The robustness of the proposed algorithm to the variation of regions-of-interest is confirmed by the Friedman tests and the p-values of which are 0.517 and 0.352 for the compression and freehand series groups, respectively. The Pearson’s correlations between the lesion sizes derived by the proposed algorithm and those defined by the average manually delineated boundaries are all higher than 0.990. The overlapping and difference ratios between the derived boundaries and the average manually delineated boundaries are mostly higher than 0.90 and lower than 0.13, respectively. For both series groups, all assessments conclude that the boundaries derived by the proposed algorithm be comparable to those delineated manually. Moreover, it is shown that the proposed algorithm is superior to the Chan and Vese level set method based on the paired-sample t-tests on the performance indices at a 5% significance level. (E-mail: chung@ntu.edu.tw) [Copyright &y& Elsevier]

Abstract: The classical means to measure traffic density and velocity depend on local measurements from induction loops and other on site instruments. This information does not give the whole picture of the two-dimensional traffic situation. In order to obtain precise knowledge about the traffic flow of a large area, only airborne cameras or cameras positioned at very high locations (towers, etc.) can provide an up-to-date image of all roads covered. The paper aims at showing the potential of using image time series from these cameras to derive traffic parameters on the basis of single car measurements. To be able to determine precise velocities and other parameters from an image time series, exact geocoding is one of the first requirements for the acquired image data. The methods presented here for determining several traffic parameters for single vehicles and vehicle groups involve recording and evaluating a number of digital or analog aerial images from high altitude and with a large total field of view. Visual and automatic methods for the interpretation of images are compared. It turns out that the recording frequency of the individual images should be at least 1/3 Hz (visual interpretation), but is preferably 3 Hz or more, especially for automatic vehicle tracking. The accuracy and potentials of the methods are analyzed and presented, as well as the usage of a digital road database for improving the tracking algorithm and for integrating the results for further traffic applications. Shortcomings of the methods are given as well as possible improvements regarding methodology and sensor platform. [Copyright &y& Elsevier]

This paper presents a new method for unsupervised subpixel change detection using image series. The method is based on the definition of a probabilistic criterion capable of assessing the level of coherence of an image series relative to a reference classification with a finer resolution. In opposition to approaches based on an a priori model of the data, the model developed here is based on the rejection of a nonstructured model—called a-contrario model—by the observation of structured data. This coherence measure is the core of a stochastic algorithm which automatically selects the image subdomain representing the most likely changes. A theoretical analysis of this model is led to predict its performances, in particular regarding the contrast level of the image as well as the number of change pixels in the image. Numerical simulations are also presented that confirm the high robustness of the method and its capacity to detect changes impacting more than 25 percent of a considered pixel under average conditions. An application to land-cover change detection is then provided using time series of satellite images. [ABSTRACT FROM PUBLISHER]