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Image Inpainting Based on Exemplar and Sparse Representation
Lei Zhang,Baosheng Kang,Benting Liu,Fei Zhang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.9
We propose a novel image inpainting approach in which the exemplar and the sparse representation are combined together skillfully. In the process of image inpainting, often there will be such a situation: although the sum of squared differences (SSD) of exemplar patch is the smallest among all the candidate patches, there may be a noticeable visual discontinuity in the recovered image when using the exemplar patch to replace the target patch. In this case, we cleverly use the sparse representation of image over a redundant dictionary to recover the target patch, instead of using the exemplar patch to replace it, so that we can promptly prevent the occurrence and accumulation of errors, and obtain satisfied results. Experiments on a number of real and synthetic images demonstrate the effectiveness of proposed algorithm, and the recovered images can better meet the requirements of human vision.
Visual Tracking Algorithm Based on Probabilistic Graphical Model
Mingjie Zhang,Baosheng Kang 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.9
In complicated scene, in order to solve the temporal occlusion problem of target tracking, a novel particle filter tracking algorithm based on graphical model is proposed. Graphical model is applied to particle filtering in this method. Firstly, dividing the target into several key regions, and extracting the characteristic value of each region. Then, these regions are applied to construct graphical model. In the process of target tracking by using particle filter method, graphical models can compensate for the lacking information of the occluded region. The state of the occluded part can be inferred by the graphical model. Finally, experimental results have demonstrated that the proposed tracking algorithm is effective, and it can reliably track moving target.