Ming‐Huwi Horng

2.1k total citations
46 papers, 1.3k citations indexed

About

Ming‐Huwi Horng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Ming‐Huwi Horng has authored 46 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 7 papers in Media Technology. Recurrent topics in Ming‐Huwi Horng's work include Medical Image Segmentation Techniques (10 papers), Image Enhancement Techniques (9 papers) and Image Processing Techniques and Applications (6 papers). Ming‐Huwi Horng is often cited by papers focused on Medical Image Segmentation Techniques (10 papers), Image Enhancement Techniques (9 papers) and Image Processing Techniques and Applications (6 papers). Ming‐Huwi Horng collaborates with scholars based in Taiwan. Ming‐Huwi Horng's co-authors include Yung‐Nien Sun, Yung-Nien Sun, Chih-Feng Chao, Xi‐Zhang Lin, Tai‐Hua Yang, Xiaojun Lin, Cheng‐Wei Yang, Yu‐Ming Liao, Nan‐Haw Chow and Shumin Chen and has published in prestigious journals such as Expert Systems with Applications, Anesthesiology and Materials.

In The Last Decade

Ming‐Huwi Horng

43 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming‐Huwi Horng Taiwan 17 605 431 213 197 180 46 1.3k
William G. Wee United States 17 496 0.8× 524 1.2× 245 1.2× 72 0.4× 63 0.3× 79 1.5k
Adrian Barbu United States 19 810 1.3× 292 0.7× 300 1.4× 107 0.5× 145 0.8× 73 1.5k
Sheng Huang China 24 574 0.9× 403 0.9× 164 0.8× 59 0.3× 123 0.7× 165 2.0k
Hong Song China 17 562 0.9× 268 0.6× 315 1.5× 186 0.9× 57 0.3× 182 1.4k
Nizamettin Aydın Türkiye 24 405 0.7× 386 0.9× 164 0.8× 42 0.2× 66 0.4× 140 2.4k
Dong Zhao China 18 495 0.8× 906 2.1× 174 0.8× 44 0.2× 98 0.5× 50 1.9k
Kumar Abhishek India 14 404 0.7× 362 0.8× 154 0.7× 40 0.2× 65 0.4× 49 1.2k
Li Song China 25 1.5k 2.4× 261 0.6× 95 0.4× 138 0.7× 190 1.1× 242 2.1k
Jin Zhou China 17 392 0.6× 236 0.5× 56 0.3× 247 1.3× 43 0.2× 98 1.3k
Idit Diamant Israel 11 686 1.1× 868 2.0× 219 1.0× 57 0.3× 89 0.5× 19 1.9k

Countries citing papers authored by Ming‐Huwi Horng

Since Specialization
Citations

This map shows the geographic impact of Ming‐Huwi Horng'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 Ming‐Huwi Horng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Huwi Horng more than expected).

Fields of papers citing papers by Ming‐Huwi Horng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Huwi Horng. 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 Ming‐Huwi Horng. The network helps show where Ming‐Huwi Horng may publish in the future.

Co-authorship network of co-authors of Ming‐Huwi Horng

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

All Works

20 of 20 papers shown
1.
Chen, Po-Chun, et al.. (2025). Enhancing prediction of magnetic properties in additive manufacturing products through a 3D convolutional vision transformer model. The International Journal of Advanced Manufacturing Technology. 137(9-10). 4503–4519. 1 indexed citations
2.
Tsai, Mi‐Ching, et al.. (2024). Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction. The International Journal of Advanced Manufacturing Technology. 132(1-2). 83–98. 7 indexed citations
3.
4.
Lin, Ching‐Chih, et al.. (2022). Deep Learning Applied to Defect Detection in Powder Spreading Process of Magnetic Material Additive Manufacturing. Materials. 15(16). 5662–5662. 15 indexed citations
5.
Huang, Min‐Hsin, Ming‐Huwi Horng, Chung-I Li, et al.. (2022). Validation of a Deep Learning–based Automatic Detection Algorithm for Measurement of Endotracheal Tube–to–Carina Distance on Chest Radiographs. Anesthesiology. 137(6). 704–715. 3 indexed citations
6.
Yang, Tai‐Hua, et al.. (2022). Scaphoid Fracture Detection by Using Convolutional Neural Network. Diagnostics. 12(4). 895–895. 23 indexed citations
7.
Horng, Ming‐Huwi, et al.. (2021). The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm. Applied Sciences. 11(11). 4857–4857. 2 indexed citations
8.
Horng, Ming‐Huwi, Cheng‐Wei Yang, Yung‐Nien Sun, & Tai‐Hua Yang. (2020). DeepNerve: A New Convolutional Neural Network for the Localization and Segmentation of the Median Nerve in Ultrasound Image Sequences. Ultrasound in Medicine & Biology. 46(9). 2439–2452. 31 indexed citations
9.
Horng, Ming‐Huwi. (2017). Fine-Tuning Parameters of Deep Belief Networks Using Artificial Bee Colony Algorithm. DEStech Transactions on Computer Science and Engineering. 5 indexed citations
10.
Horng, Ming‐Huwi. (2013). Multilevel Image Thresholding with Glowworm Swam Optimization Algorithm based on the Minimum Cross Entropy. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences. 5(10). 1290–1298. 4 indexed citations
11.
Horng, Ming‐Huwi. (2012). Multilevel Minimum Cross Entropy Threshold Selection with Shuffled Frog-leaping Algorithm. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences. 4(11). 168–176. 2 indexed citations
12.
Horng, Ming‐Huwi. (2011). Multilevel image thresholding by using the shuffled frog-leaping optimization algorithm. 12. 144–149. 2 indexed citations
13.
Horng, Ming‐Huwi, et al.. (2010). Image vector quantization algorithm via honey bee mating optimization. Expert Systems with Applications. 38(3). 1382–1392. 34 indexed citations
14.
Horng, Ming‐Huwi & Shumin Chen. (2009). Multi-class Classification of Ultrasonic Supraspinatus Images based on Radial Basis Function Neural Network. Journal of Medical and Biological Engineering. 29(5). 242–250. 7 indexed citations
15.
Horng, Ming‐Huwi. (2007). An ultrasonic image evaluation system for assessing the severity of chronic liver disease. Computerized Medical Imaging and Graphics. 31(7). 485–491. 20 indexed citations
16.
Horng, Ming‐Huwi, Yung-Nien Sun, & Xi‐Zhang Lin. (2002). A diagnostic image system for assessing the severity of chronic liver disease. 3. 1672–1675. 3 indexed citations
17.
Lin, Xi‐Zhang, Yung‐Nien Sun, Ming‐Huwi Horng, & Xiaozhen Guo. (2002). Computer morphometry for liver fibrosis using an automatic image analysis system. 2. 682–683. 2 indexed citations
18.
Horng, Ming‐Huwi, Yung-Nien Sun, & Xi‐Zhang Lin. (2002). Texture feature coding method for classification of liver sonography. Computerized Medical Imaging and Graphics. 26(1). 33–42. 73 indexed citations
19.
Sun, Yung‐Nien & Ming‐Huwi Horng. (1997). Assessing liver tissue fibrosis with an automatic computer morphometry system. IEEE Engineering in Medicine and Biology Magazine. 16(3). 66–73. 7 indexed citations
20.
Sun, Yung‐Nien, et al.. (1996). Ultrasonic image analysis for liver diagnosis. IEEE Engineering in Medicine and Biology Magazine. 15(6). 93–101. 40 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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