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We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from unmanned aerial vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood, utilizing calculated masks over each component of the LAB colorspace, as well as using an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying hysteresis thresholding segmentation. The proposed method is tested and compared with variations and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-score, respectively. The results and computational efficiency of the proposed method show that it is suitable for onboard data execution and decision-making during UAV flights. [ABSTRACT FROM AUTHOR]

The paper presents Visit Planner (ViP), a mobile application prototype that provides a solution to the challenging tourist trip design problem. ViP follows a holistic approach offering personalized recommendations for Points of Interest (POIs) based on preferences either explicitly collected by the application, or inferred by the users' ongoing interaction with the system. ViP proposes to the final user, a trajectory of POIs calculated using an Expectation Maximization method that maximizes user satisfaction taking into consideration a variety of time and spatial constraints for both users and POIs. Additionally, POIs are divided into categories, so that a certain number of POIs from each category to be included in the final itinerary. The application is implemented as a user-interactive system that allows the flexibility for easy content adaptation and facilitates management of content and services by the user. The prototype has been implemented for Android-based smartphones, on an open application environment, using standard communication protocols and open database technology. Currently, it is applied to the city of Agios Nikolaos in Crete, and is available for download from Google play. [ABSTRACT FROM AUTHOR]

In recent years, the scientific and technological developments have led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user's personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on the criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, keyframe selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines. [ABSTRACT FROM AUTHOR]

The scientific impact and rankings are considered important metrics for determining national recognition and prestige, as well as for determining scientific priorities and funding. This paper analyzes the distribution of leading scientists across all scientific fields and countries, examining the similarity graphs between them. Our analysis also reveals further regional and continental patterns. By measuring the correlation between top scientists with GPD data, we show that the ratio of scientists maintains a high correlation with the GDP per capita. We also study the link between the scientific performances of a country (in terms of top research personnel it hosts) with its economic wealth. Finally, we analyze the case of Greece and show that the scientific impact of a country changes significantly when we take into consideration its scientific personnel that has migrated abroad (i.e., brain-drain). [ABSTRACT FROM AUTHOR]

In this paper, we present a general version of polygonal fitting problem called Unconstrained Polygonal Fitting (UPF). Our goal is to represent a given 2D shape S with an N-vertex polygonal curve P with a known number of vertices, so that the Intersection over Union (IoU) metric between S and P is maximized without any assumption or prior knowledge of the object structure and the location of the N-vertices of P that can be placed anywhere in the 2D space. The search space of the UPF problem is a superset of the classical polygonal approximation (PA) problem, where the vertices are constrained to belong in the boundary of the given 2D shape. Therefore, the resulting solutions of the UPF may better approximate the given curve than the solutions of the PA problem. For a given number of vertices N, a Particle Swarm Optimization (PSO) method is used to maximize the IoU metric, which yields almost optimal solutions. Furthermore, the proposed method has also been implemented under the equal area principle so that the total area covered by P is equal to the area of the original 2D shape to measure how this constraint affects IoU metric. The quantitative results obtained on more than 2800 2D shapes included in two standard datasets quantify the performance of the proposed methods and illustrate that their solutions outperform baselines from the literature. [ABSTRACT FROM AUTHOR]

A jigsaw puzzle is a recreational activity that involves assembling a certain number of pieces into a combined and well-fitting unit without creating gaps between adjacent pieces. Two-dimensional puzzles are divided into two main categories, the "apictorial" in which the only information available is the shape of the pieces and the "pictorial" which may take into account not only the shape of the pieces, but also their content. Jigsaw puzzles are considered as one of the most popular category of puzzles. The majority of them are accompanied by a guiding image and there is only one "counterpart" for each side of each piece (pictorial jigsaw puzzles), although some more difficult variants have blank pieces, the so-called apictorial jigsaw puzzles. In this paper, we will examine the open problem of solving pictorial and apictorial jigsaw puzzles, and their various applications, such as the reconstruction of two-dimensional fragmented objects, the restoration of fragmented wall-paintings and the repair of shredded documents. We will also present an evaluation of the state-of-the- art jigsaw puzzle reassembly techniques in pictorial and apictorial puzzles. [ABSTRACT FROM AUTHOR]

In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a "winner": the most preferable variant of our hybrid recommender algorithm is one using a 〈four elicitation slates, six shown images per slate〉 pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process. [ABSTRACT FROM AUTHOR]

The scientific impact and rankings of individual persons, institutions, or nations are very important for their international recognition and prestige. At the same time, they are also vital for the governments, businesses and trusts which must decide scientific priorities and funding. In this paper we show that the ranking of a country changes significantly when we take into consideration its scientific personnel that has migrated abroad (i.e., brain-drain). Given that immigrant scientists are considered recoverable assets who can always return home, we believe that governments, businesses and trusts should anticipate immigrant scholars when deciding their scientific priorities, optimizing their research allocations, re-orienting their research support, or augmenting research productivity. [ABSTRACT FROM AUTHOR]

This paper studies the problem of unsupervised detection of geometrically similar fragments (segments) in curves, in the context of boundary matching. The goal is to determine all pairs of sub-curves that are geometrically similar, under local scale invariance. In particular, we aim to locate the existence of a similar section (independent of length and/or orientation) in the second curve, to a section of the first curve, as indicated by the user. The proposed approach is based on a suitable distance matrix of the two given curves. Additionally, a suitable objective function is proposed to capture the trade-off between the similarity of the common sub-sequences and their lengths. The goal of the algorithm is to minimize this objective function via an efficient graph-based approach that capitalizes on Dynamic Time Warping to compare the two subcurves. We apply the proposed technique in the context of geometric matching of coastline pairs. This application is crucial for investigating the forcing factors related to the coastline evolution. The proposed method was successfully applied to global coastline data, yielding a bipartite graph with analytical point-to-point correspondences. [ABSTRACT FROM AUTHOR]

Parents may rely on media content classification systems such as those on the Common Sense Media website to help make decisions about what films are appropriate for their child. In this paper, we examine the relationships between Common Sense Media "expert" age/content ratings for 7132 films and the age recommendations made by parents commenting on the site. In addition to examining the extent of correspondence between these two sources of age classification, we examine whether "expert" ratings of the level of particular types of content in each film was associated with parents' age classifications. Our research results indicate that age ratings of one out of five parents of our sample data cannot be predicted by the expert ratings. In general, parents tended to have stricter age recommendations than the Common Sense Media experts. Regression analyses indicate that parent age ratings showed (successively weaker) associations with expert ratings of the extent to which each film contained adult language, sex, violence, scary content, educational value, substance use, and consumerism. Prior State of Knowledge: Information and communication technologies have modified the way children entertain themselves, but also the complexity of parental mediation. Parents rely on content classification systems to help them make informed decisions about audiovisual content in linear TV but also on video-on-demand. Novel Contributions: We show that a percentage of parents cannot rely on experts' age ratings, because they have different views on what is appropriate for their children. In addition, we try to explore how several factors influence parents' views on age classification. Practical Implications: The study draws parents' and policymakers' attention, as we elucidate that audiovisual classification systems should follow a more personalized, parent-centric approach, so that parents are able to make informed decisions following their parenting views, without being suppressed by the norm. [ABSTRACT FROM AUTHOR]

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks. [ABSTRACT FROM AUTHOR]

We propose a Dual Hybrid Recommender System based on SCoR, the Synthetic Coordinate Recommendation system, and the Random Forest method. By combining user ratings and user/item features, SCoR is initially employed to provide a recommendation which is fed into the Random Forest. The two systems are initially combined by splitting the training set into two "equivalent" parts, one of which is used to train SCoR while the other is used to train the Random Forest. This initial approach does not exhibit good performance due to reduced training. The resulted drawback is alleviated by the proposed dual training system which, using an innovative splitting method, exploits the entire training set for SCoR and the Random Forest, resulting to two recommender systems that are subsequently efficiently combined. Experimental results demonstrate the high performance of the proposed system on the Movielens datasets. [ABSTRACT FROM AUTHOR]

Recommender systems try to provide users with accurate personalized suggestions for items based on an analysis of previous user decisions and the decisions made by other users. These systems suffer from profile injection attacks, where malicious profiles are generated in order to promote or demote a particular item introducing abnormal ratings. The problem of automatic detection of such malicious profiles has been recently addressed by a great number of authors in the literature using supervised and unsupervised approaches. In this paper, we propose a framework to identify anomalous rating profiles, where each attacker (outlier) hurriedly creates profiles that inject into the system an unspecified combination of random ratings and specific ratings, without any prior knowledge of the existing ratings. This attack is a superset of the two different attacks (Uniform and Delta) proposed in Harper et al. (ACM Trans Interact Intell Syst 5(4):19, 2016) making the attack model more realistic and its detection more challenging. The proposed detection method is based on several attributes related to the unpredictable behavior of the outliers in a validation set, on the user-item rating matrix, on the similarity between users and on the filler items. In this work, we propose a new attribute (RIS) to capture the randomness in item selection of the abnormal profiles. In this work, three different systems are proposed: (1) a probabilistic framework that estimates the probability of a user to be an outlier by combining several features in a completely unsupervised way. (2) An unsupervised clustering system based on the k-means algorithm that automatically spots the spurious profiles. (3) A supervised framework that uses a random forest classifier for cases where labeling sample data is available. Experimental results on the MovieLens and the Small Netflix datasets demonstrate the high performance of the proposed methods as well as the discrimination accuracy of the proposed features. [ABSTRACT FROM AUTHOR]

The authors define and approximately solve the problem of unsupervised image sorting that is considered as a kind of content‐based image clustering. The content‐based image sorting is the creation of a route that passes through all the images once, in such an order that the next one from the previous image has similar content. In the end, an image ordering (e.g. slideshow) is automatically produced, so that the images with similar content should be close to each other. This problem resembles the problem known in the literature as 'travelling salesman problem' (TSP). In this work, the authors have proposed two classes of methods (the nearest‐neighbour and genetic methods) that have also been applied on the TSP problem. Their benefits on computational efficiency and accuracy are discussed over six datasets that have been created from the GHIM‐10K dataset. The experimental results demonstrate that the proposed methods efficiently solve the image sorting problem, producing image sequences that almost agree with human intuition. [ABSTRACT FROM AUTHOR]

Scope of the present work is to apply modern methods of pattern recognition concerning the automatic detection of geomorphological features (curvilinear lineaments and topographic highs), with emphasis on geological faults and to compare the results of the automatic detection with aerial photographs and further geological data collected in Tinos Island. The contribution of this work in the geosciences is multidisciplinary because (a) it proposes to geoscientists a new tool of geomorphological analysis prior to the fieldwork and especially at the stage where published work and already available data are collected, evaluated and reprocessed, (b) it increases the detection accuracy of the geological features both prior and after the fieldwork, (c) it decreases the time of fieldwork and consequently the cost of the entire geological research and (d) it can be applied in digital elevation data. The automatically detected linear-curvilinear lineaments and topographic highs agree in location, shape and orientation with the ground truth data (geological maps, aerial photographs and field measurements). Furthermore, the shape and the orientation of the most prominent topographic high (Tsiknias) seems to be related to the tectonic regime in the wide area of study corresponding to the ENE-WSW, WSW-ESE, N-S and E-W trending normal faults. [ABSTRACT FROM AUTHOR]

In this paper, we propose an unsupervised point clustering framework. The goal is to cluster N given points into K clusters, so that similarities between objects in the same group are high while the similarities between objects in different groups are low. The point similarity is defined by a voting measure that takes into account the point distances. Using the voting formulation, the problem of clustering is reduced to the maximization of the sum of votes between the points of the same cluster. We have shown that the resulting clustering based on voting maximization has advantages concerning the cluster's compactness, working well for clusters of different densities and/or sizes. In addition, the proposed scheme is able to detect outliers. Experimental results and comparisons to existing methods on real and synthetic datasets demonstrate the high performance and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a solution on microaggregation problem based on the hierarchical tree equi-partition (HTEP) algorithm. Microaggregation is a family of methods for statistical disclosure control of microdata, that is, for masking microdata, so that they can be released without disclose private information on the underlying individuals. Knowing that the microaggregation problem is non-deterministic polynomial-time-hard, the goal is to partitionNgiven data into groups of at leastKitems, so that the sum of the within-partition squared error is minimized. The proposed method is general and it can be applied to any tree partition problem aiming at the minimization of a total score. The method is divisive, so that the tree with the highest ‘score’ is split into two trees, resulting in a hierarchical forest of trees with almost equal ‘score’ (equipartition). We propose a version of HTEP for microaggregation (HTEPM), that is applied on the minimum spanning tree (MST) of the graph defined by the data. The merit of the HTEPM algorithm is that it solves optimally some instances of the multivariate microaggregation problem on MST search space in. Experimental results and comparisons with existing methods from literature prove the high performance and robustness of HTEPM. [ABSTRACT FROM AUTHOR]

We present a model-based, top-down solution to the problem of tracking the 3D position, orientation and full articulation of the human body from markerless visual observations obtained by two synchronized RGBD cameras. Inspired by recent advances to the problem of model-based hand tracking Oikonomidis et al. (Efficient Model-based 3D Tracking of Hand Articulations using Kinect, ), we treat human body tracking as an optimization problem that is solved using stochastic optimization techniques. We show that the proposed approach outperforms in accuracy state of the art methods that rely on a single RGBD camera. Thus, for applications that require increased accuracy and can afford the extra-complexity introduced by the second sensor, the proposed approach constitutes a viable solution to the problem of markerless human motion tracking. Our findings are supported by an extensive quantitative evaluation of the method that has been performed on a publicly available data set that is annotated with ground truth. [ABSTRACT FROM AUTHOR]

Geological faults comprise an interesting and of high-importance subject of study in geosciences, since they are responsible for seismic activity in regions where plate boundaries (divergent, convergent, transform) are present. In this paper, we propose a framework for automatic enhancement and identification of the linear patterns of geological fault structures. According to the proposed method, first, we compute the Slope and Aspect images, as well as their derivatives. Then, a rotation- and scale-invariant filter and a pixel-labeling method provide an enhancement of the geological faults and the detection of points that probably belong to them, respectively. The enhancement and detection results are efficiently combined to construct a weighted graph that represents the possible fault points and the connections between them taking into account the shape and topology of the faults. Thus, the detection of the linear fault patterns, which is the main goal of this framework, is reduced to the problem of suitable subset selection, corresponding to the graph edges of the most representative faults. Concerning the experimental results, the proposed method has been tested on several onshore and offshore real topographic images yielding high-performance results. [ABSTRACT FROM PUBLISHER]

A fundamental problem in networking and computing is community detection. Various applications like finding web communities, uncovering the structure of social networks, or even analyzing a graph's structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, represented by an undirected, unweighted graph, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. Experimental results and comparisons with the Lancichinetti et al. method (Phys. Rev. E 80(5 Pt 2), 056117, ; New J. Phys. 11(3), 033015, ) are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters. Emphasis is given on benchmark graphs with significant variations in the size of their communities. Further experimental results are presented for two real dataset graphs, namely the Enron, and the Epinions graphs, from SNAP, the Stanford Large Network Dataset Collection. The experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient clustering algorithm that has been applied to the microaggregation problem. The goal is to partition $(N)$ given records into clusters, each of them grouping at least $(K)$ records, so that the sum of the within-partition squared error (SSE) is minimized. We propose a successive Group Selection algorithm that approximately solves the microaggregation problem in $(O(N^2 \log N))$ time, based on sequential Minimization of SSE. Experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in social networking with a lot of potential applications is to detect effectively the communities that are created by the users' interaction. Other applications, such as finding web communities, uncovering the structure of social networks, or even analyzing a graph's structure to uncover Internet attacks are equally as important. All these problems converge to a common goal, the development of flexible and efficient local community detection algorithms. In this paper, we demonstrate the performance of an algorithm that uncovers the entire community structure of a network, based solely on local interactions between neighboring nodes and a distributed clustering algorithm. The proposed algorithm, named VCD, is based on the distributed computation of a synthetic coordinates for each graph node. Experimental results and comparisons with another method from the literature (Lancichinetti et al.) are presented. The algorithm is also tested on two real dataset graphs from the SNAP: Stanford Large Network Dataset Collection. In all cases the experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency. [ABSTRACT FROM AUTHOR]

We propose a general purpose image segmentation framework, which involves feature extraction and classification in feature space, followed by flooding and merging in spatial domain. Region growing is based on the computed local measurements and distances from the distribution of features describing the different classes. Using the properties of the label dependent distances spatial coherence is ensured, since the image features are described globally. The distribution of the features for the different classes are obtained by block-wise unsupervised clustering based on the construction of the minimum spanning tree of the blocks' grid using the Mallows distance and the equipartition of the resulting tree. The final clustering is obtained by using the k-centroids algorithm. With high probability and under topological constraints, connected components of the maximum likelihood classification map are used to compute a map of initially labelled pixels. An efficient flooding algorithm is introduced, namely, Priority Multi-Class Flooding Algorithm (PMCFA), that assign pixels to labels using Bayesian dissimilarity criteria. A new region merging method, which incorporates boundary information, is introduced for obtaining the final segmentation map. Therefore, the merging stage is based on region features and edge localization. Segmentation results on the Berkeley benchmark data set demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method for automatic enhancement and identification of partial curvilinear structures. The accurate identification of line structures in geophysical images plays an important role in geophysical interpretation and the detection of subsurface structures. The method was applied on geophysical images in an effort to recognize the linear patterns of subsurface architectural structures that exist in archaeological sites. To our knowledge, the problem of identification of curvilinear structures in geophysical images for archaeological sites is faced for the first time. The method efficiently combines a rotation- and scale-invariant filter and a pixel-labeling method, providing a robust enhancement and detection of mostly line structures in 2-D grayscale images, respectively. Experimental results on real and synthetic images and comparison with existing methods in the literature indicated the reliable performance of the proposed scheme. [ABSTRACT FROM PUBLISHER]

We present a key frames selection algorithm based on three Iso-content principles (Iso-Content Distance, Iso-Content Error and Iso-Content Distortion), so that the selected key frames are equidistant-in video content according to the used principle. Two automatic approaches for defining the most appropriate number of key frames are proposed by exploiting supervised and unsupervised content criteria. Experimental results and the comparisons with existing methods from literature on large dataset of real-life video sequences illustrate the high performance of the proposed schemata. [ABSTRACT FROM AUTHOR]

We present a shape-based method for automatic people detection and counting without any assumption concerning camera motion. In order to evaluate the robustness of the proposed method, we apply it for classifying athletics videos into two classes: videos of individual and videos of team sports. The videos used are real and characterized by dynamic and unconstrained environment. Moreover, in the case of team sport, we propose a shape deformations based method for running/hurdling discrimination (activity recognition). Robust, adaptive and independent from color, illumination changes and the camera motion, the proposed features are combined in the Transferable Belief Model (TBM) framework providing a two-level (frames and shot) video categorization. Experimental results of 97% of accuracy for individual/team sport categorization using a dataset of 252 real videos of athletic meetings, acquired by moving cameras under varying view angles, indicate the stability and the good performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for the automatic identification of P-phase arrival based on the distribution of local maxima (LM) in earthquake seismograms. The method efficiently combines energy and frequency characteristics of the LM distribution (LMD). The P detection is mainly based on the energy of a seismic event in the case the earthquake has higher amplitude than seismic background noise. Otherwise, it is based on the frequency of LM. Thus, the method provides robust detection of P-phase arrival in any quality type of seismic data. Moreover, it uses two sequential sliding signal windows yielding very high accuracy on the P-phase estimation. A hierarchical P-phase detection algorithm dramatically reduces the computational cost, making possible a real-time implementation. Experimental results from a large database of more than 80 low, medium, and high signal-to-noise ratio seismic events and comparison with existing methods in the literature indicate the reliable performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods. [ABSTRACT FROM AUTHOR]

A geophysical investigation was carried across the M3 burial mound from Silvașu de Jos —Dealu Țapului, a tumuli necropolis in western Romania, where the presence of the Yamnaya people was certified archaeologically. For characterizing the inner structure of the mound, two conventional geophysical methods have been used: a geomagnetic survey and electrical resistivity tomography (ERT). The results allowed the mapping of the central features of the mound and the establishment of the relative stratigraphy of the mantle, which indicated at least two chronological phases. Archaeological excavations performed in the central part of the mound accurately validated the non-invasive geophysical survey and offered a valuable chronological record of the long-forgotten archaeological monument. Geophysical approaches proved to be an invaluable instrument for the exploration of the monument and suggest a fast constructive tool for the investigation of the entire necropolis which currently has a number of distinct mounds. [ABSTRACT FROM AUTHOR]

Detailed information regarding land utilization/cover is a valuable resource in various fields. In recent years, remote sensing images, especially aerial images, have become higher in resolution and larger span in time and space, and the phenomenon that the objects in an identical category may yield a different spectrum would lead to the fact that relying on spectral features only is often insufficient to accurately segment the target objects. In convolutional neural networks, down-sampling operations are usually used to extract abstract semantic features, which leads to loss of details and fuzzy edges. To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant channel information from the backbone to optimize the learned context. Secondly, the proposed Multi-level Feature Aggregation Upsample module nestedly uses the idea that high-level features provide guidance information for low-level features, which is of great significance for positioning the restoration of high-resolution remote sensing images. Finally, the proposed Channel Ladder Refinement module is used to refine the restored high-resolution feature maps. Experimental results show that the proposed method achieves state-of-the-art performance 86.45% mean IOU on LandCover dataset. [ABSTRACT FROM AUTHOR]

Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method is proposed to reconstruct SAIR brightness temperature distribution. Different from classical single-parameter regularization, the multi-parameter regularization adds multiple different penalties which can exhibit multi-scale characteristics of the original distribution. Multiple regularization parameters are selected by use of the simplified multi-dimensional generalized cross-validation method. The experimental results show that, compared with the conventional total variation, Tikhonov, and band-limited regularization methods, the multi-parameter regularization method can retain more detailed information and better improve the accuracy of the reconstructed brightness temperature distribution, and exhibit superior noise suppression, demonstrating the effectiveness and the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This work presents novel pattern recognition techniques applied on bathymetric data from two large areas in Eastern Mediterranean. Our objectives are as follows: (a) to demonstrate the efficiency of this methodology, (b) to highlight the quick and accurate detection of both hydrocarbon related tectonic lineaments and salt structures affecting seafloor morphology, and (c) to reveal new structural data in areas poised for hydrocarbon exploration. In our work, we first apply a multiple filtering and sequential skeletonization scheme inspired by the hysterisis thresholding technique. In a second stage, we categorize each linear and curvilinear segment on the seafloor skeleton (medial axis) based on the strength of detection as well as the length, direction, and spatial distribution. Finally, we compare the seafloor skeleton with ground truth data. As shown in this paper, the automatic extraction of the bathymetric skeleton allows the interpretation of the most prominent seafloor morphological features. We focus on the competent tracing of tectonic lineaments, as well as the effective distinction between seafloor features associated with shallow evaporite movements and those related to intense tectonic activity. The proposed scheme has low computational demand and decreases the cost of the marine research because it facilitates the selection of targets prior to data acquisition. [ABSTRACT FROM AUTHOR]