Min-Chun Yang

498 total citations
12 papers, 351 citations indexed

About

Min-Chun Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Min-Chun Yang has authored 12 papers receiving a total of 351 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Media Technology. Recurrent topics in Min-Chun Yang's work include Image and Signal Denoising Methods (5 papers), Advanced Image Processing Techniques (5 papers) and Advanced Vision and Imaging (3 papers). Min-Chun Yang is often cited by papers focused on Image and Signal Denoising Methods (5 papers), Advanced Image Processing Techniques (5 papers) and Advanced Vision and Imaging (3 papers). Min-Chun Yang collaborates with scholars based in Taiwan, United States and South Korea. Min-Chun Yang's co-authors include Yu-Chiang Frank Wang, De-An Huang, Ruey‐Feng Chang, Li‐Wei Kang, Chiun‐Sheng Huang, Chia‐Wen Lin, Min Sun Bae, Jeon‐Hor Chen, Woo Kyung Moon and Yi-Ren Yeh and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging and Ultrasound in Medicine & Biology.

In The Last Decade

Min-Chun Yang

12 papers receiving 335 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Min-Chun Yang Taiwan	9	276	129	88	63	18	12	351
Mansur Vafadust Iran	9	356 1.3×	205 1.6×	49 0.6×	81 1.3×	28 1.6×	18	470
Nilamani Bhoi India	12	197 0.7×	56 0.4×	40 0.5×	155 2.5×	11 0.6×	28	332
Lingraj Dora India	7	166 0.6×	44 0.3×	116 1.3×	70 1.1×	32 1.8×	19	314
Masoud Faraki Australia	10	163 0.6×	52 0.4×	70 0.8×	10 0.2×	7 0.4×	15	230
Yurun Ma China	8	137 0.5×	38 0.3×	116 1.3×	64 1.0×	22 1.2×	16	276
Yugen Yi China	11	247 0.9×	53 0.4×	69 0.8×	159 2.5×	24 1.3×	33	374
Zhi-Song Liu Hong Kong	10	368 1.3×	170 1.3×	30 0.3×	15 0.2×	6 0.3×	24	418
Bingqian Lin China	11	252 0.9×	25 0.2×	226 2.6×	37 0.6×	20 1.1×	21	391
Sudip Kumar Adhikari India	7	216 0.8×	78 0.6×	61 0.7×	38 0.6×	3 0.2×	14	285

Countries citing papers authored by Min-Chun Yang

Since Specialization

Citations

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

Fields of papers citing papers by Min-Chun Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min-Chun Yang. 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 Min-Chun Yang. The network helps show where Min-Chun Yang may publish in the future.

Co-authorship network of co-authors of Min-Chun Yang

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

All Works

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12 of 12 papers shown

1.

Yang, Min-Chun, et al.. (2018). Context-aware Cascade Attention-based RNN for Video Emotion Recognition. 1–6. 17 indexed citations

2.

Yang, Min-Chun, Chia-Po Wei, Yi-Ren Yeh, & Yu-Chiang Frank Wang. (2015). Recognition at a long distance: Very low resolution face recognition and hallucination. 237–242. 23 indexed citations

3.

Yang, Min-Chun, et al.. (2015). Query-Adaptive Multiple Instance Learning for Video Instance Retrieval. IEEE Transactions on Image Processing. 24(4). 1330–1340. 8 indexed citations

4.

Yang, Min-Chun, et al.. (2014). Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition. 17 indexed citations

5.

Yang, Min-Chun, Woo Kyung Moon, Yu-Chiang Frank Wang, et al.. (2013). Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis. IEEE Transactions on Medical Imaging. 32(12). 2262–2273. 75 indexed citations

6.

Yang, Min-Chun, Chiun‐Sheng Huang, Jeon‐Hor Chen, & Ruey‐Feng Chang. (2012). Whole Breast Lesion Detection Using Naive Bayes Classifier for Portable Ultrasound. Ultrasound in Medicine & Biology. 38(11). 1870–1880. 17 indexed citations

7.

Yang, Min-Chun, et al.. (2012). Self-Learning of Edge-Preserving Single Image Super-Resolution via Contourlet Transform. 574–579. 4 indexed citations

8.

Yang, Min-Chun & Yu-Chiang Frank Wang. (2012). A Self-Learning Approach to Single Image Super-Resolution. IEEE Transactions on Multimedia. 15(3). 498–508. 90 indexed citations

9.

Huang, De-An, et al.. (2012). Context-aware single image super-resolution using locality-constrained group sparse representation. 1–6. 9 indexed citations

10.

Huang, De-An, Li‐Wei Kang, Min-Chun Yang, Chia‐Wen Lin, & Yu-Chiang Frank Wang. (2012). Context-Aware Single Image Rain Removal. 164–169. 73 indexed citations

11.

Yang, Min-Chun, et al.. (2011). Learning context-aware sparse representation for single image super-resolution. 1349–1352. 12 indexed citations

12.

Chang, Ruey‐Feng, Min-Chun Yang, Woo Kyung Moon, et al.. (2008). Computer-aided diagnosis of breast color elastography. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6915. 69150I–69150I. 6 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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