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RISS 인기검색어
- 검색키워드
- 저자 : ( Zhengping Hu ) (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Saliency Detection based on Global Color Distribution and Active Contour Analysis
( Zhengping Hu ),( Zhenbin Zhang ),( Zhe Sun ),( Shuhuan Zhao ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12
In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.
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Saliency Detection based on Global Color Distribution and Active Contour Analysis
Hu, Zhengping,Zhang, Zhenbin,Sun, Zhe,Zhao, Shuhuan Korean Society for Internet Information 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12
In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.
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Moving Object Detection Using Sparse Approximation and Sparse Coding Migration
( Shufang Li ),( Zhengping Hu ),( Mengyao Zhao ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.5
In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.
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Boosting oxygen reduction catalysis with abundant copper single atom active sites
Li, Feng,Han, Gao-Feng,Noh, Hyuk-Jun,Kim, Seok-Jin,Lu, Yalin,Jeong, Hu Young,Fu, Zhengping,Baek, Jong-Beom The Royal Society of Chemistry 2018 Energy & environmental science Vol.11 No.8
<P>With their high catalytic activity, stability, selectivity, and 100% atom utilization, single atomic non-noble metal based materials are valuable alternatives to efficient but expensive Pt based catalysts. For efficient catalysis, single-atom catalysts must expose abundant single atomic metal active centers. Here, we report the rational design and synthesis of a Cu single-atom catalyst with high Cu content of over 20.9 wt%, made of single atomic Cu anchored into an ultrathin nitrogenated two-dimensional carbon matrix (Cu-N-C). The high Cu content was achieved by the introduction of additional N species, which can securely trap and protect the Cu atoms. During oxygen reduction, the single atomic Cu exhibited over 54 times higher mass activity than metallic Cu nanoparticles at a potential of 0.85 V <I>versus</I> a reversible hydrogen electrode (RHE). Furthermore, the Cu-N-C exhibited 3.2 times higher kinetic current at 0.85 V (<I>vs.</I> RHE), and a much lower Tafel slope (37 mV dec<SUP>−1</SUP>), as well as better methanol/carbon monoxide tolerance and long-term stability than commercial Pt/C. Density functional theory (DFT) calculations reveal that the Cu active sites exhibit improved O-O bond stretching and favorable adsorption energies of O2 and OOH for four-electron oxygen reduction.</P>
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