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20132022
most citedSequential vessel segmentation via deep channel attention network

78 citations · 187 across the 9 of their papers we have counts for

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Showing cs.CVShow all

6 papers · 1 filter

cs.CV2022

Working memory inspired hierarchical video decomposition with transformative representations

Binjie Qin, Haohao Mao, Ruipeng Zhang +3

Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e.g., extracting movi…

cs.CV2021★ 78 cited

Sequential vessel segmentation via deep channel attention network

Dongdong Hao, Song Ding, Linwei Qiu +4

This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current…

cs.CV2018★ 38 cited

Texture variation adaptive image denoising with nonlocal PCA

Wenzhao Zhao, Qiegen Liu, Yisong Lv +1

Image textures, as a kind of local variations, provide important information for human visual system. Many image textures, especially the small-scale or stochastic textures are ric…

cs.CV2013

Scale Selection of Adaptive Kernel Regression by Joint Saliency Map for Nonrigid Image Registration

Zhuangming Shen, Jiuai Sun, Hui Zhang +1

Joint saliency map (JSM) [1] was developed to assign high joint saliency values to the corresponding saliency structures (called Joint Saliency Structures, JSSs) but zero or low jo…

cs.CV2013★ 21 cited

Registration of Images with Outliers Using Joint Saliency Map

Binjie Qin, Zhijun Gu, Xianjun Sun +1

Mutual information (MI) is a popular similarity measure for image registration, whereby good registration can be achieved by maximizing the compactness of the clusters in the joint…

cs.CV2013

Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression

Binjie Qin, Zhuangming Shen, Zien Zhou +5

For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than m…