Hongju Lu

Prof. Hongju Lu

School of Management, Guangzhou City University of Technology, Guangzhou, Guangdong, China

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2020 in Signal Processing
Verified
Journal Article
Closed access
JCJia Cui
Jia Cui
QCQianqian Cai
Qianqian Cai
HLHongju Lu
Hongju Lu
ZJZhenlin Jia
Zhenlin Jia
MTMingxi Tang
Mingxi Tang

1 November 2021 in Neurocomputing
Verified
Journal Article
Closed access
JCJ. Cui
J. Cui
YLY.Q. Liu
Y.Q. Liu
HLHongju Lu
Hongju Lu
QCQ.Q. Cai
Q.Q. Cai
MTM.X. Tang
M.X. Tang
ZGZ.Y. Gu
Z.Y. Gu

7 September 2023 in Buildings
Verified
Journal Article
Open access
PDF file
JCJia Cui
Jia Cui
MZmengwwi zang
mengwwi zang
Zhen LiuZhen Liu
Zhen Liu
MQMeng Qi
Meng Qi
RLRong Luo
Rong Luo
HLHongju Lu
Hongju Lu
The rapid increase in building components on the building information model (BIM) object database has created new demand for BIM product recommendations to improve design efficiency. Current efforts mainly focus on the shape and contents of the products, instead of stylistic consistency, which is a crucial factor during the practical design process. To tackle such a problem, this paper proposes a novel framework to capture stylistic features based on long-range design dependencies with structural preservation, of which the snapshots of BIM products have been used to extract the stylistic features; core patches with strong style, generated by the pre-trained saliency model, are the root nodes; stylistic correlations are calculated as the hyperedges by tree-based operations; deep features and design features are proposed to represent the low-level and style distribution based on the study of design theory; and an ensemble learning strategy is introduced to solve the unbalanced classifier performance. An ablation study is conducted to validate the effectiveness of the proposed framework, in which comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

10 April 2018
Verified
Conference Paper
Closed access
PDF file
HLHongju Lu
Hongju Lu
JCJia Cui
Jia Cui
RLRui Liu
Rui Liu
YFYuanbin Fu
Yuanbin Fu
YZYanhui Zhao
Yanhui Zhao
PYPengfei Yue
Pengfei Yue
Image retargeting technique requires important information preservation and less edge distortion during increasing/decreasing image size. The major existed content-aware methods perform well. However, there are two problems should be improved: the slight distortion appeared at the object edges and the structure distortion in the nonsalient area. According to psychological theories, people evaluate image quality based on multi-level judgments and comparison between different areas, both image content and image structure. The paper proposes a new standard: the structure preserving in non-salient area. After observation and image analysis, blur (slight blur) is generally existed at the edge of objects. The blur feature is used to estimate the depth cue, named blur depth descriptor. It can be used in the process of saliency computation for balanced image retargeting result. In order to keep the structure information in nonsalient area, the salient edge map is presented in Seam Carving process, instead of field-based saliency computation. The derivative saliency from x- and y-direction can avoid the redundant energy seam around salient objects causing structure distortion. After the comparison experiments between classical approaches and ours, the feasibility of our algorithm is proved.

6 July 2024 in Digital Signal Processing
Verified
Journal Article
Closed access
JCJia Cui
Jia Cui
HJHao Jiang
Hao Jiang
MQMeng Qi
Meng Qi
ZGZhenyu Gu
Zhenyu Gu
HLHongju Lu
Hongju Lu