Search Results for author: Beiji Zou

Found 10 papers, 2 papers with code

Structure-Aware NeRF without Posed Camera via Epipolar Constraint

1 code implementation1 Oct 2022 Shu Chen, Yang Zhang, Yaxin Xu, Beiji Zou

This two-stage strategy is not convenient to use and degrades the performance because the error in the pose extraction can propagate to the view synthesis.

Novel View Synthesis

Estimation of 3D Human Pose Using Prior Knowledge

no code implementations9 May 2021 Shu Chen, Lei Zhang, Beiji Zou

Estimating three-dimensional human poses from the positions of two-dimensional joints has shown promising results. However, using two-dimensional joint coordinates as input loses more information than image-based approaches and results in ambiguity. In order to overcome this problem, we combine bone length and camera parameters with two-dimensional joint coordinates for input. This combination is more discriminative than the two-dimensional joint coordinates in that it can improve the accuracy of the model's prediction depth and alleviate the ambiguity that comes from projecting three-dimensional coordinates into two-dimensional space.

Pose Estimation

A Deep Retinal Image Quality Assessment Network with Salient Structure Priors

no code implementations31 Dec 2020 Ziwen Xu, Beiji Zou, Qing Liu

Dual-branch SalStructIQA contains two CNN branches and one is guided by large-size salient structures while the other is guided by tiny-size salient structures.

Image Quality Assessment

A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation

no code implementations5 Nov 2019 Qing Liu, Beiji Zou, Yang Zhao, Yixiong Liang

To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.

DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation

no code implementations18 Apr 2019 Qing Liu, Xiaopeng Hong, Wei Ke, Zailiang Chen, Beiji Zou

In this paper, we propose a novel segmentation approach, named Cartesian-polar dual-domain network (DDNet), which for the first time considers the complementary of the Cartesian domain and the polar domain.

Feature Importance

Feature Selection via Sparse Approximation for Face Recognition

no code implementations14 Feb 2011 Yixiong Liang, Lei Wang, Yao Xiang, Beiji Zou

Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades.

Face Recognition

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