Entity

Time filter

Source Type


Zhang B.,Shandong University | Chen C.,Shandong University | Yan H.,Shandong University of Finance and Economics | Liu W.,Hisense State Key Laboratory of Digital Multi Media Technology Corporation
International Conference on Signal Processing Proceedings, ICSP | Year: 2010

Image registration is an important task in the field of computer vision and pattern recognition. In this paper, we propose a robust sub-pixel registration algorithm which is based on multi-resolution and new edge detection interpolation method. After applying truncated window function to the images to be registration, the low-pass bands of the wavelet decomposition are applied to build the image pyramid. Performance of our proposed algorithm is compared with other method using the same block matching algorithm and mutual information similarity measure. The results show that our proposed algorithm is effective for noisy degradation condition and can lead to highly accurate image registration. © 2010 IEEE. Source


Yuan H.,Shandong University | Liu J.,Shandong University | Li Z.,Applied Technology Internet | Liu W.,Hisense State Key Laboratory of Digital Multi Media Technology Corporation
Electronics Letters | Year: 2012

Depth is one of the most important parameters in three dimensional videos (3DV). To improve the coding efficiency of depth maps, a virtual view oriented distortion criterion is proposed to substitute the mean squared error during the rate distortion optimisation process. By employing the proposed distortion criterion, the bit rates of depth maps could be reduced while maintaining the same quality of synthesised virtual views. Experimental results demonstrate that an average of -6.89 and a maximum of -23.04 coding bit rate reduction of depth maps could be achieved while maintaining the same quality of synthesised virtual view. © 2012 The Institution of Engineering and Technology. Source


Nie X.-S.,Shandong University | Nie X.-S.,Shandong University of Finance and Economics | Liu J.,Shandong University | Liu J.,Hisense State Key Laboratory of Digital Multi Media Technology Corporation | And 2 more authors.
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | Year: 2011

A video hashing based on Locally Linear Embedding (LLE) is proposed in this paper. In this method, some representative frames are first selected based on a graph model, and four-order cumulants are taken as features of video in the high dimensional feature space. Then the video is mapped to a three-dimensional space using LLE, and video hash sequence is generated using the norms of points in the three-dimensional space to detect video copies. Experimental results show that the video hashing has good robustness and discrimination. Source


Nie X.,Shandong University | Nie X.,Shandong University of Finance and Economics | Liu J.,Shandong University | Sun J.,Shandong University | Liu W.,Hisense State Key Laboratory of Digital Multi Media Technology Corporation
IEEE Signal Processing Letters | Year: 2011

A robust video hashing scheme for video content identification and authentication is proposed, which is called Double-Layer Embedding scheme. Intra-cluster Locally Linear Embedding (LLE) and inter-cluster Multi-Dimensional Scaling (MDS) are used in the scheme. Some dispersive frames of the video are first selected through graph model, and the video is partitioned into clusters based on the dispersive frames and the K-Nearest Neighbor method during the hashing. Then, the intra-cluster LLE and inter-cluster MDS are used to generate local and global hash sequences which can inherently describe the corresponding video. Experimental results show that the video hashing is resistant to geometric attacks of frames and channel impairments of transmission. © 2006 IEEE. Source


Wu Q.,Shandong University | Liu J.,Shandong University | Liu J.,Hisense State Key Laboratory of Digital Multi Media Technology Corporation | Sun J.,Shandong University | And 2 more authors.
Journal of Computational Information Systems | Year: 2012

In this paper, we propose a new shift-invariant feature extraction method based on tensor analysis for robust text-independent speaker Identification. Multiple factors including time, frequency, scale and phase are investigated to extract the essential features of speech signal. In order to explore the shift-invariant characteristic of spectral features, we propose a convolutive model to learn the spectral basis function in tensor structure. Nonnegative assumption is also imposed on the CANDECOMP model to ensure the sparsity and preserve robust features. Experimental results demonstrate that our proposed method can improve the recognition accuracy specifically in noise conditions. © 2012 Binary Information Press. Source

Discover hidden collaborations