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Image Cropping by Patches Dissimilarities
Shangbing Gao,Youdong Zhang,Wanli Feng,Dashan Chen 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.8
Image cropping is a technique to help people improve their taken photos’ quality by discarding unnecessary parts of a photo. A novel method is presented for Image cropping by patches dissimilarities. In this paper, we propose a novel patches dissimilarities algorithm. Firstly, representing the image patches and reducing dimensionality. Then we extract the visual saliency map of these photos based on the patches dissimilarities. Finally, by the saliency map and face priors, we find a cropped region that can be best found. The experimental results demonstrate that our technique is applicable to a wide range of photos and produce more agreeable resulting photos.
A New Connected Coherence Tree Algorithm For Image Segmentation
( Jingbo Zhou ),( Shangbing Gao ),( Zhong Jin ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.4
In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.
A Novel Flame Edge Detection Algorithm via a Novel Active Contour Model
Wanli Feng,Ying Li,Shangbing Gao,Yunyang Yan,Jianxun Xue 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.9
Flame edge detection from color images is a challenging research area recently. In this paper, an extension of active contour model is proposed by adding the two information types to both internal and external energy terms. Therefore, the combination of these two forces allows for flexible initialization of the contours. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Edge extraction of different flame images using C-V model and the classical edge detection operators are compared and analyzed. Experimental results show that the existing methods do not emphasize the continuity and clarity of the flame and fire edges while the proposed method identifies the continuous and clear edges of the flame fire.
Identity-based Deniable Authenticated Encryption for E-voting Systems
( Chunhua Jin ),( Guanhua Chen ),( Jianyang Zhao ),( Shangbing Gao ),( Changhui Yu ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.6
Deniable authentication (DA) is a protocol in which a receiver can generate an authenticator that is probabilistically indistinguishable from a sender. DA can be applied in many scenarios that require user privacy protection. To enhance the security of DA, in this paper, we construct a new deniable authenticated encryption (DAE) scheme that realizes deniable authentication and confidentiality in a logical single step. Compared with existing approaches, our approach provides proof of security and is efficient in terms of performance analysis. Our scheme is in an identity-based environment; thus, it avoids the public key certificate-based public key infrastructure (PKI). Moreover, we provide an example that shows that our protocol is applicable for e-voting systems.