Tyng-Luh Liu

4.2k total citations · 1 hit paper
68 papers, 2.5k citations indexed

About

Tyng-Luh Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Tyng-Luh Liu has authored 68 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Computer Vision and Pattern Recognition, 17 papers in Artificial Intelligence and 10 papers in Signal Processing. Recurrent topics in Tyng-Luh Liu's work include Advanced Image and Video Retrieval Techniques (17 papers), Advanced Vision and Imaging (14 papers) and Image Retrieval and Classification Techniques (12 papers). Tyng-Luh Liu is often cited by papers focused on Advanced Image and Video Retrieval Techniques (17 papers), Advanced Vision and Imaging (14 papers) and Image Retrieval and Classification Techniques (12 papers). Tyng-Luh Liu collaborates with scholars based in Taiwan, United States and Slovenia. Tyng-Luh Liu's co-authors include Hwann-Tzong Chen, Shang‐Hong Lai, Yen‐Yu Lin, Chiou‐Shann Fuh, Davi Geiger, Horng-Horng Lin, Jen‐Hui Chuang, Ding-Jie Chen, Hsien-Tzu Cheng and Min Sun and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Tyng-Luh Liu

65 papers receiving 2.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Local Discriminant Embedding and Its Variants

2005 447 citations Hwann-Tzong Chen, Tyng-Luh Liu et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tyng-Luh Liu Taiwan	22	2.2k	509	384	182	173	68	2.5k
Liang-Tien Chia Singapore	23	1.7k 0.8×	511 1.0×	319 0.8×	236 1.3×	280 1.6×	106	2.3k
Hwann-Tzong Chen Taiwan	22	2.2k 1.0×	366 0.7×	285 0.7×	153 0.8×	326 1.9×	57	2.5k
Dacheng Tao Australia	24	2.1k 1.0×	921 1.8×	308 0.8×	138 0.8×	108 0.6×	54	2.9k
Deepu Rajan Singapore	28	2.2k 1.0×	256 0.5×	460 1.2×	180 1.0×	60 0.3×	121	2.8k
Chenglizhao Chen China	28	1.8k 0.8×	287 0.6×	398 1.0×	42 0.2×	68 0.4×	92	2.1k
Jianjun Lei China	35	3.0k 1.4×	352 0.7×	762 2.0×	686 3.8×	113 0.7×	163	3.7k
Hongliang Li China	35	4.1k 1.9×	479 0.9×	1.1k 2.9×	728 4.0×	119 0.7×	215	4.5k
Zhuolin Jiang United States	16	2.1k 0.9×	853 1.7×	458 1.2×	276 1.5×	715 4.1×	37	2.7k
Weiming Dong China	26	2.1k 1.0×	388 0.8×	356 0.9×	116 0.6×	133 0.8×	118	2.7k

Countries citing papers authored by Tyng-Luh Liu

Since Specialization

Citations

This map shows the geographic impact of Tyng-Luh Liu'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 Tyng-Luh Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tyng-Luh Liu more than expected).

Fields of papers citing papers by Tyng-Luh Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tyng-Luh Liu. 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 Tyng-Luh Liu. The network helps show where Tyng-Luh Liu may publish in the future.

Co-authorship network of co-authors of Tyng-Luh Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Tyng-Luh Liu. A scholar is included among the top collaborators of Tyng-Luh Liu 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 Tyng-Luh Liu. Tyng-Luh Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Chen, Hsiu-Ling, et al.. (2025). Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer. BMC Neurology. 25(1). 5–5.

2.

Yeh, Chun‐Fu, Wei‐Jen Chen, Cheng-Han Tsai, et al.. (2024). Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia. Journal of Assisted Reproduction and Genetics. 41(11). 3179–3187.

3.

Cheng, Yiwei, et al.. (2023). Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning. Journal of Clinical Monitoring and Computing. 38(2). 271–279. 2 indexed citations

4.

Hsieh, He‐Yen, et al.. (2023). Aggregating Bilateral Attention for Few-Shot Instance Localization. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 6314–6323. 5 indexed citations

5.

Yang, Chia-Lin, et al.. (2021). ezGeno: an automatic model selection package for genomic data analysis. Bioinformatics. 38(1). 30–37.

6.

Lin, Hsuan-Tien, et al.. (2021). Adaptive and Generative Zero-Shot Learning. International Conference on Learning Representations. 17 indexed citations

7.

Chen, Ding-Jie, et al.. (2021). Learning Unsupervised Metaformer for Anomaly Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4349–4358. 2 indexed citations

8.

Chen, Ding-Jie, He‐Yen Hsieh, & Tyng-Luh Liu. (2021). Adaptive Image Transformer for One-Shot Object Detection. 12242–12251. 46 indexed citations

9.

Ke, Tsung-Wei & Tyng-Luh Liu. (2016). Recursive reduction net for large-scale high-dimensional data. 16. 1903–1907. 1 indexed citations

10.

Hwang, Jyh-Jing & Tyng-Luh Liu. (2015). Pixel-wise Deep Learning for Contour Detection. arXiv (Cornell University). 11 indexed citations

11.

Liu, Tyng-Luh, et al.. (2015). Spatio-Temporal Learning of Basketball Offensive Strategies. 1123–1126. 9 indexed citations

12.

Liu, Tyng-Luh, et al.. (2013). A Two-dimensional Direct Combined Model for Facial Hallucination.. 149–152. 4 indexed citations

13.

Liu, Tyng-Luh, et al.. (2011). Fusing generic objectness and visual saliency for salient object detection. 914–921. 262 indexed citations

14.

Lin, Horng-Horng, Jen‐Hui Chuang, & Tyng-Luh Liu. (2010). Regularized Background Adaptation: A Novel Learning Rate Control Scheme for Gaussian Mixture Modeling. IEEE Transactions on Image Processing. 20(3). 822–836. 90 indexed citations

15.

Liu, Tyng-Luh, et al.. (2009). Learning partially-observed hidden conditional random fields for facial expression recognition. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 533–540. 34 indexed citations

16.

Lin, Horng-Horng, Tyng-Luh Liu, & Jen‐Hui Chuang. (2009). Learning a Scene Background Model via Classification. IEEE Transactions on Signal Processing. 57(5). 1641–1654. 51 indexed citations

17.

Lin, Yen‐Yu, Tyng-Luh Liu, & Chiou‐Shann Fuh. (2007). Local Ensemble Kernel Learning for Object Category Recognition. NTUR (臺灣機構典藏). 1–8. 72 indexed citations

18.

Lin, Horng-Horng, Tyng-Luh Liu, & Jen‐Hui Chuang. (2003). A probabilistic SVM approach for background scene initialization. Proceedings - International Conference on Image Processing. 1. 893–896. 41 indexed citations

19.

Liu, Tyng-Luh & Hwann-Tzong Chen. (2002). A variational approach for digital watermarking. 2. 674–677. 1 indexed citations

20.

Liu, Tyng-Luh & Davi Geiger. (1999). Approximate tree matching and shape similarity. 456–462 vol.1. 88 indexed citations

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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