Search Results for author: Binjie Qin

Found 7 papers, 2 papers with code

Working memory inspired hierarchical video decomposition with transformative representations

1 code implementation21 Apr 2022 Binjie Qin, Haohao Mao, Ruipeng Zhang, Yueqi Zhu, Song Ding, Xu Chen

Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e. g., extracting moving contrast-filled vessels from the complex and noisy backgrounds of X-ray coronary angiography (XCA).

Computational Efficiency Retrieval +1

Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography

no code implementations16 Apr 2022 Binjie Qin, Haohao Mao, Yiming Liu, Jun Zhao, Yisong Lv, Yueqi Zhu, Song Ding, Xu Chen

Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved.

feature selection Rolling Shutter Correction +1

Sequential vessel segmentation via deep channel attention network

1 code implementation10 Feb 2021 Dongdong Hao, Song Ding, Linwei Qiu, Yisong Lv, Baowei Fei, Yueqi Zhu, Binjie Qin

To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks.

Decoder

Texture variation adaptive image denoising with nonlocal PCA

no code implementations26 Oct 2018 Wenzhao Zhao, Qiegen Liu, Yisong Lv, Binjie Qin

For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures.

Clustering Image Denoising

Registration of Images with Outliers Using Joint Saliency Map

no code implementations29 Mar 2013 Binjie Qin, Zhijun Gu, Xianjun Sun, Yisong Lv

However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and biased maxima.

Image Registration

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

no code implementations3 Mar 2013 Zhuangming Shen, Jiuai Sun, HUI ZHANG, Binjie Qin

JSM guides the local structure matching in nonrigid registration by emphasizing these JSSs' sparse deformation vectors in adaptive kernel regression of hierarchical sparse deformation vectors for iterative dense deformation reconstruction.

Image Registration regression

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

no code implementations3 Feb 2013 Binjie Qin, Zhuangming Shen, Zien Zhou, Jiawei Zhou, Jiuai Sun, HUI ZHANG, Mingxing Hu, Yisong Lv

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 matching the common structures with small deformations in the two images.

Image Registration regression

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