Geometric model fitting has been widely applied in the electronic industry. However, it remains as a challenging task when handling the data corrupted by a large number of false matches (i.e., severe ...outliers) between two-view images. In this article, we propose a novel motion consistency guided fitting method (MCF) to robustly and efficiently estimate the parameters of model instances in data involving severe outliers. Specifically, from input data, we first generate a series of neighborhood sets, in each of which gross outliers that are inconsistent in motions can be effectively filtered, according to motion consistency among true matches (i.e., inliers). Then, we propose an effective sampling algorithm to sample minimal subsets from the generated neighborhood sets. In this way, the model hypotheses computed from the sampled minimal subsets can cover all model instances with a high probability. Furthermore, by taking advantages of the generated hypotheses and neighborhood sets, we propose a novel model selection algorithm to estimate the number and the parameters of model instances. For fitting evaluation, we also build a new dataset, in which the images are collected from a fundus camera. Experiments on a variety of electronic industrial applications show that the proposed MCF achieves higher fitting accuracy at a much lower computational cost than several state-of-the-art fitting methods.
Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle ...high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.
Owing to the vagueness of real-world environments and limited knowledge of human beings, it is natural for decision makers to express their preferences by means of incomplete 2-tuple fuzzy linguistic ...preference relations (FLPRs). Besides, since different decision makers possess distinctive educational and academic background, they may select linguistic term sets with different granularities to represent their opinions. This study primarily focuses on multi-granular linguistic multi-criteria group decision-making (MCGDM) based on incomplete 2-tuple FLPRs. First, we introduce an extended four-way procedure for estimating unknown elements from an acceptable incomplete 2-tuple FLPR as well as a completion method for obtaining unknown linguistic 2-tuples in an unacceptable incomplete 2-tuple FLPR. Based on the four ways for estimating unknown values, a formula is then established to calculate the consistency index of an incomplete 2-tuple FLPR. Second, a 2-tuple linguistic induced generalized ordered weighted averaging operator is introduced and further analyzed from different aspects. Subsequently, by systematically fusing the aforementioned contributions, a novel approach is developed to address MCGDM problems with multi-granular incomplete 2-tuple FLPRs and unknown weight information. Specifically, the importance weights of decision makers with respect to different criteria are decided by the corresponding consistency indices. Finally, an investment problem is furnished to illustrate the application of our proposed approach.
In this article, the stochastic form of the Newell-Whitehead-Segel equation has been investigated. This is a fully nonlinear partial differential equation and has huge applications. The nonlinearity ...of the underlying problem leads to the fact that one has to do the nonlinear analysis of the problem. So, firstly this article describes the regularity of the solution in the context of existing theory and a new approach has been applied to show the existence of the solution and corresponding explicit a-priori estimates of the Schauder type have been proposed. Secondly, in the next part, we have proposed two numerical schemes for the solution of the underlying problem and both schemes are very fighting for consistency and stability. The obtained numerical results are reliable, time-efficient, and very much adjacent to the exact state of the unknown function.
The next generation of cosmic microwave background experiments will produce cosmic variance limited observations over a large fraction of sky and for a large range of multipoles. In this work we ...discuss different consistency tests that can be performed with the upcoming data from the Simons Observatory and the Planck data. We quantify the level of expected cosmological parameter shifts probed by these tests. We discuss the effect of difference in frequency of observation and present forecasts on a direct measurement of the Planck T-to-E leakage beam. We find that instrumental systematics in either of the experiments will be assessed with an exquisite precision, well beyond the intrinsic uncertainties due to the cosmic microwave background cosmic variance.
In this paper, the maximum spacing method is considered for multivariate observations. Nearest neighbour balls are used as a multidimensional analogue to univariate spacings. A class of ...information-type measures is used to generalize the concept of maximum spacing estimators. Weak and strong consistency of these generalized maximum spacing estimators are proved both when the assigned model class is correct and when the true density is not a member of the model class. An example of the generalized maximum spacing method in model validation context is discussed.
Video summarization targets to extract the most important segments from a video by spatiotemporal analysis. Previous methods primarily learn content within videos based on appearance information, ...with a rare discussion on the effective utilization of motion information, which is equally essential to video understanding. In this letter, we expound upon a Motion-Assisted Reconstruction Network (MAR-Net), which synergistically models appearance and motion information within videos for unsupervised video summarization without any manual annotations. MAR-Net notably comprises a Bidirectional Modality Encoder (BiME) and a Video Context Navigator (VCN). By integrating uni-modal and cross-modal feature aggregation into a unified module, BiME allows for exploring sophisticated dependency relationships among features through a bidirectional attention mechanism. VCN can promote the semantic consistency between the cross-modal contexts and the input video by a consistency loss term, alleviating the noisy impact within the motion stream. Empirical results conducted on benchmark datasets demonstrate that MAR-Net outperforms other state-of-the-art methods.
Intuitionistic multiplicative preference relations (IMPRs) have been widely applied in decision making for their ability to efficiently express the uncertainty of information. This paper investigates ...the decision making with incomplete IMPRs. First, a new consistency of incomplete IMPRs is defined. Then, we present some optimization models for estimating missing values by maximizing the consistency level. After that, the group IMPR is derived by using membership degrees in individual intuitionistic multiplicative judgments. Subsequently, a transformation method is offered to build a consistent IMPR through a normalized intuitionistic fuzzy priority weight vector. Moreover, a model is presented to get intuitionistic fuzzy priority weights. Finally, we propose a group decision making (GDM) method with incomplete IMPRs. A practical GDM problem on venue selection for communication drills is offered to indicate the specific application of main theoretical results.
In complex and heterogeneous geoenvironments, landslides exhibit varying features in different environments, and data in landslide inventories are imbalanced. Existing data-driven landslide ...susceptibility evaluation (LSE) methods overlook environmental heterogeneity and cannot reliably predict regions with few samples. Alternatively, global random negative sampling strategies may produce imbalanced positive and negative samples in some environments, contributing to inaccurate predictions. This article proposes a graph neural network (GNN) constrained by environmental consistency (GNN-EC) to overcome these problems. The GNN-EC consists of graphs with nodes, and edges. A graph represents the environmental relationships in the study area. Nodes are geographic units delineated from terrain polygon approximation. Edges capture the relationships between node-pairs. Additionally, the weights of edges reflect the similarity between two node environments. A GNN aggregates node information in the graph for LSE. Our experiment showed that the proposed method outperformed the common machine learning methods: increasing prediction accuracy by approximately 7, 5-6 and 3-4% compared to the artificial neural network (ANN), the support vector machine (SVM) and the random forest (RF), respectively. Moreover, our method can maintain high prediction accuracy, even with a small training set.
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