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RISS 인기검색어
- 검색키워드
- 저자 : ( Guijin Tang ) (검색결과 2 건)
- 무료
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- 유료
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Disparity-based Error Concealment for Stereoscopic Images with Superpixel Segmentation
( Yizhang Zhang ),( Guijin Tang ),( Xiaohua Liu ),( Changming Sun ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.9
To solve the problem of transmission errors in stereoscopic images, this paper proposes a novel error concealment (EC) method using superpixel segmentation and adaptive disparity selection (SSADS). Our algorithm consists of two steps. The first step is disparity estimation for each pixel in a reference image. In this step, the numbers of superpixel segmentation labels of stereoscopic images are used as a new constraint for disparity matching to reduce the effect of mismatching. The second step is disparity selection for a lost block. In this step, a strategy based on boundary smoothness is proposed to adaptively select the optimal disparity which is used for error concealment. Experimental results demonstrate that compared with other methods, the proposed method has significant advantages in both objective and subjective quality assessment.
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AANet: Adjacency auxiliary network for salient object detection
( Xialu Li ),( Ziguan Cui ),( Zongliang Gan ),( Guijin Tang ),( Feng Liu ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.10
At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.
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