Publications

The background subtraction is an important technique in computer vision which segments moving objects into video sequences by comparing each new frame with a learned background model. In this work, the authors propose a novel background subtraction method based on Gaussian–Bernoulli restricted Boltzmann machines (GRBMs). The GRBM is different from the ordinary restricted Boltzmann machine (RBM) by using real numbers as inputs, resulting in a constrained mixture of Gaussians, which is one of the most widely used techniques to solve the background subtraction problem. The GRBM makes it easy to learn the variance of pixel values and takes the advantage of the generative model paradigm of the RBM. They present a simple technique to reconstruct the learned background model from a given input frame and to extract the foreground from the background using the variance learned for each pixel. Furthermore, they demonstrate the effectiveness of the proposed technique with extensive experimentation and quantitative evaluation on several commonly used public data sets for background subtraction.

Supporting the objective of reaching consensus is a notable research area in the context of group decision making. Recently, several approaches based on an allocation of information granularity have been proposed for supporting it. Those approaches improve the consensus among the individuals participating in the decision problem by means of the required flexibility provided by the information granularity level. However, they do not take into account that the adjusted preferences could be very different from the preferences communicated by the individuals. Furthermore, they cannot be applied in decision problems where various criteria are considered to evaluate the choices. To overcome these shortcomings, we introduce a new consensus approach based on minimum adjustment for multi-criteria group decision making with an allocation of information granularity. An experimental study is also provided to illustrate this consensus approach and analyze its performance.

Information granulation is an effective vehicle to explore data structure and has become a timely research topic. In this article, a top-down granulation model (T-GrM) adhering to the two fundamental requirements (coverage and specificity) in the principle of justifiable granularity is designed. Here, this principle is employed as a fundamental method for constructing information granules in each layer, and as an indicator to determine whether the obtained partitions need to be further divided in the next layer. For capturing the correlation among features and reflecting the data structure, an improved version of a T-GrM is offered by incorporating the principal component analysis. The proposed granulation models are evaluated on synthetic datasets, where the obtained experimental results offer some insights into the feasibility of the designed models and describe the effect of parameter values on the constructed information granules. The improved version of the T-GrM has the advantage of being transparent by generating information granules on several principal components, and thereby, the description of information granules is simpler and descriptive. As application examples, two real-world datasets are analyzed to exhibit the advantage of the constructed information granules.

At present, independent scheduling of Earth-observation resources (EORs) is usually difficult to satisfy diverse observation requirements and cannot realize the full potential of space–air resource networks. To utilize EORs comprehensively, this study constructs a divide-and-conquer framework (DCF) for a coordinated scheduling of air and space observation resources (i.e., satellites and unmanned aerial vehicles). The DCF can decompose the original scheduling problem into a task allocation subproblem and multiple task scheduling subproblems that can be solved using a coordination planner and subplanners, respectively. For the task allocation subproblem, we propose a simulated annealing algorithm combined with variable neighborhood adjustment (SA-VNA) method, where a solution variation strategy (SVS) is designed. The SVS iteratively adjusts the task allocation scheme according to the coordinated scheduling result of the last iteration. Based on the allocation scheme, multiple task scheduling subproblems are generated, and existing effective algorithms are used to resolve them. Extensive experiments and comparative analysis show that SA-VNA outperforms several peer algorithms overall, indicating that DCF plus SA-VNA can significantly improve the efficiency of space–air resource networks.