Jun-Bao Li

701 total citations
50 papers, 489 citations indexed

About

Jun-Bao Li is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering. According to data from OpenAlex, Jun-Bao Li has authored 50 papers receiving a total of 489 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 11 papers in Media Technology and 9 papers in Aerospace Engineering. Recurrent topics in Jun-Bao Li's work include Face and Expression Recognition (21 papers), Image Retrieval and Classification Techniques (12 papers) and Remote-Sensing Image Classification (9 papers). Jun-Bao Li is often cited by papers focused on Face and Expression Recognition (21 papers), Image Retrieval and Classification Techniques (12 papers) and Remote-Sensing Image Classification (9 papers). Jun-Bao Li collaborates with scholars based in China, Taiwan and Australia. Jun-Bao Li's co-authors include Jeng‐Shyang Pan, Zhe‐Ming Lu, Lujia Han, Haiyan Zhang, Minsheng Lu, Yaping Li, Ping Fu, Mingzhu Xu, Huijun Gao and Zhi He and has published in prestigious journals such as Bioresource Technology, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Jun-Bao Li

45 papers receiving 460 citations

Peers

Countries citing papers authored by Jun-Bao Li

Since Specialization

Citations

This map shows the geographic impact of Jun-Bao Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun-Bao Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun-Bao Li more than expected).

Fields of papers citing papers by Jun-Bao Li

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun-Bao Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun-Bao Li. The network helps show where Jun-Bao Li may publish in the future.

Co-authorship network of co-authors of Jun-Bao Li

This figure shows the co-authorship network connecting the top 25 collaborators of Jun-Bao Li. A scholar is included among the top collaborators of Jun-Bao Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jun-Bao Li. Jun-Bao Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Title	Journal	Authors	Indexed citations
1	Siamsdt: a self-adaptive dynamic template siamese network for airborne visual tracking of MAVs on heterogeneous FPGA-SoC	The Journal of Supercomputing	Yuxin Zhang, Ran Wu et al.	0
2	A thermo-chemo-mechanical coupled polycrystalline phase field model for thermal barrier coating under CMAS infiltration and corrosion	European Journal of Mechanics - A/Solids	Zhang Na, Jun-Bao Li et al.	0
3	Cross-Spectral Gaussian Splatting with Spatial Occupancy Consistency	Proceedings of the AAAI Conference on Artificial Intelligence	Hong Guo, Huanyu Liu et al.	0
4	The synergetic effect of ultrafine grinding and alkaline hydrogen peroxide pretreatment for the enzymatic saccharification of corn stover	Industrial Crops and Products	Jun-Bao Li, Suan Shi et al.	4
5	A Transformer-based network with adaptive spatial prior for visual tracking	Neurocomputing	Gaoliang Peng, Jun-Bao Li et al.	0
6	Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images	Remote Sensing	Pengcheng Wang, Huanyu Liu et al.	4
7	UAV Autonomous Navigation Based on Deep Reinforcement Learning in Highly Dynamic and High-Density Environments	Drones	Jun-Bao Li, Qi Han et al.	7
8	Hyperspectral Image Recognition Using SVM Combined Deep Learning	網際網路技術學刊	Yifan Li, Jun-Bao Li et al.	17
9	Insights into the improvement of alkaline hydrogen peroxide (AHP) pretreatment on the enzymatic hydrolysis of corn stover: Chemical and microstructural analyses	Bioresource Technology	Jun-Bao Li, Minsheng Lu et al.	76
10	The Classification of Synthetic Aperture Radar Image Target Based on Deep Learning.	J. Inf. Hiding Multim. Signal Process.	Xiuyuan Chen, Xiyuan Peng et al.	3
11	Overview of Deep Kernel Learning Based Techniques and Applications.		Xiuyuan Chen, Xiyuan Peng et al.	7
12	Optimizing matrix mapping with data dependent kernel for image classification		Delong Zhang, Jun-Bao Li et al.	8
13	Multiple data-dependent kernel for classification of hyperspectral images	Expert Systems with Applications	Zhi He, Jun-Bao Li	18
14	Quasi-static and Modal Analysis of Bridge-Type Compliant Mechanism with Flexure Hinges		Zongquan Deng, Xiang Wu et al.	1
15	Gabor filter based optical image recognition using Fractional Power Polynomial model based common discriminant locality preserving projection with kernels	Optics and Lasers in Engineering	Jun-Bao Li	7
16	Quasiconformal kernel common locality discriminant analysis with application to breast cancer diagnosis	Information Sciences	Jun-Bao Li, Yu Peng et al.	11
17	Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition	Neurocomputing	Jun-Bao Li, Jeng‐Shyang Pan et al.	18
18	A Novel Optimal Bus Design Method for Modular Spacecraft.		Dan Cui, Jun-Bao Li	1
19	Discriminant Feature Fusion Strategy for Supervised Learning		Jun-Bao Li, Shu‐Chuan Chu et al.	3
20	A Novel Matrix Norm Based Gaussian Kernel for Feature Extraction of Images		Jun-Bao Li, Shu‐Chuan Chu et al.	2

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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