Showing papers by "Hui Li published in 2014"
TL;DR: Experimental results indicate that the proposed method not only works well under different complex backgrounds with less data storage, but also outperforms some existing methods in both subjective and objective qualities.
Abstract: A novel approach to detect an infrared small target in the compressive domain is presented. First, the original infrared image is projected on a sensing matrix to obtain the measurement vector. Then the target and the background are recovered simultaneously from the measurements based on low-rank and sparse matrix decomposition. Experimental results indicate that the proposed method not only works well under different complex backgrounds with less data storage, but also outperforms some existing methods in both subjective and objective qualities.
31 citations
06 Jul 2014
TL;DR: This paper investigates the construction of multiobjective test problems with complicated POFs, of which its local parts could have mixed dimensionalities, and formulate eight test problems, called CPFT1-8, with such a feature.
Abstract: It is well-established that the shapes of Pareto-optimal fronts (POFs) can affect the performance of some multiobjective optimization methods The most well-known characteristics on the shape of POFs are convexity and discontinuity In this paper, we investigate the construction of multiobjective test problems with complicated POFs, of which its local parts could have mixed dimensionalities For example, in the case of 3 objectives, some parts of POFs can be 1-D curves while others could be 2-D surfaces We formulate eight test problems, called CPFT1-8, with such a feature To study the difficulties of these test problems, we conducted some experiments with two state-of-the-art algorithms MOEA/D and NSGA-II, and analyzed their performances
23 citations