Leonardo Chang

539 total citations
26 papers, 301 citations indexed

About

Leonardo Chang is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Leonardo Chang has authored 26 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Vision and Pattern Recognition, 9 papers in Signal Processing and 6 papers in Artificial Intelligence. Recurrent topics in Leonardo Chang's work include Face recognition and analysis (10 papers), Face and Expression Recognition (9 papers) and Biometric Identification and Security (8 papers). Leonardo Chang is often cited by papers focused on Face recognition and analysis (10 papers), Face and Expression Recognition (9 papers) and Biometric Identification and Security (8 papers). Leonardo Chang collaborates with scholars based in Mexico, United Kingdom and Cuba. Leonardo Chang's co-authors include Miguel González-Mendoza, Heydi Méndez-Vázquez, Luis Enrique Sucar, Gilberto Ochoa‐Ruiz, Sharib Ali, Miguel Arias-Estrada, Eduardo F. Morales, R. Biscay, Marta Gomez‐Barrero and Christoph Busch and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Medical Image Analysis.

In The Last Decade

Leonardo Chang

25 papers receiving 297 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leonardo Chang Mexico 11 224 106 51 34 28 26 301
Mengran Gou United States 9 200 0.9× 44 0.4× 64 1.3× 59 1.7× 10 0.4× 11 263
Shibao Zheng China 8 269 1.2× 158 1.5× 85 1.7× 30 0.9× 8 0.3× 23 356
Rizhao Cai Singapore 10 246 1.1× 221 2.1× 40 0.8× 14 0.4× 41 1.5× 23 320
Stefan Petscharnig Austria 8 166 0.7× 31 0.3× 52 1.0× 44 1.3× 50 1.8× 14 291
Klaus Schöffmann Austria 9 276 1.2× 25 0.2× 81 1.6× 23 0.7× 23 0.8× 13 352
Tingting Chai China 8 140 0.6× 195 1.8× 51 1.0× 18 0.5× 4 0.1× 31 263
Harald Ganster Austria 8 140 0.6× 56 0.5× 68 1.3× 26 0.8× 5 0.2× 22 269
Toshanlal Meenpal India 9 214 1.0× 62 0.6× 20 0.4× 37 1.1× 12 0.4× 42 289
Menglong Yang China 10 191 0.9× 86 0.8× 36 0.7× 42 1.2× 5 0.2× 21 315
Lifeng Wang China 9 101 0.5× 45 0.4× 62 1.2× 8 0.2× 29 1.0× 36 283

Countries citing papers authored by Leonardo Chang

Since Specialization
Citations

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

Fields of papers citing papers by Leonardo Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leonardo Chang. 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 Leonardo Chang. The network helps show where Leonardo Chang may publish in the future.

Co-authorship network of co-authors of Leonardo Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Chang. A scholar is included among the top collaborators of Leonardo Chang 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 Leonardo Chang. Leonardo Chang 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.
González-Mendoza, Miguel, et al.. (2024). Knowledge Distillation in Video-Based Human Action Recognition: An Intuitive Approach to Efficient and Flexible Model Training. Journal of Imaging. 10(4). 85–85. 1 indexed citations
2.
González-Mendoza, Miguel, et al.. (2023). An Overview of the Vision-Based Human Action Recognition Field. Mathematical and Computational Applications. 28(2). 61–61. 7 indexed citations
3.
Chang, Leonardo, et al.. (2023). A Rare Complication of Henoch-Schönlein Purpura/IgA Vasculitis in an Adult Woman After COVID-19 Infection. Cureus. 15(7). e42063–e42063. 1 indexed citations
4.
Ochoa‐Ruiz, Gilberto, et al.. (2022). Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion. Medical Image Analysis. 81. 102569–102569. 29 indexed citations
5.
Chang, Leonardo, et al.. (2022). Action recognition by key trajectories. Pattern Analysis and Applications. 25(2). 409–423. 2 indexed citations
6.
Méndez-Vázquez, Heydi, et al.. (2021). Benchmarking lightweight face architectures on specific face recognition scenarios. Artificial Intelligence Review. 54(8). 6201–6244. 37 indexed citations
7.
Méndez-Vázquez, Heydi, et al.. (2021). Lightweight Low-Resolution Face Recognition for Surveillance Applications. SPIRE - Sciences Po Institutional REpository. 5421–5428. 13 indexed citations
8.
Méndez-Vázquez, Heydi, et al.. (2021). Towards Accurate and Lightweight Masked Face Recognition: An Experimental Evaluation. IEEE Access. 10. 7341–7353. 17 indexed citations
9.
10.
Chang, Leonardo, et al.. (2021). Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. 1824–1827. 15 indexed citations
11.
Chang, Leonardo, et al.. (2019). Utilidad de las Escalas de Gradación en el Tratamiento Quirúrgico de Malformaciones Arteriovenosas Cerebrales. Surgical Neurology International. 10(Suppl 1). S46–S57.
12.
13.
Chang, Leonardo, et al.. (2019). Effective and Generalizable Graph-Based Clustering for Faces in the Wild. Computational Intelligence and Neuroscience. 2019. 1–12. 3 indexed citations
14.
Méndez-Vázquez, Heydi, et al.. (2019). ShuffleFaceNet: A Lightweight Face Architecture for Efficient and Highly-Accurate Face Recognition. SPIRE - Sciences Po Institutional REpository. 2721–2728. 52 indexed citations
15.
Biscay, R., et al.. (2018). On Fisher vector encoding of binary features for video face recognition. Journal of Visual Communication and Image Representation. 51. 155–161. 17 indexed citations
16.
Méndez-Vázquez, Heydi, et al.. (2018). Toward More Realistic Face Recognition Evaluation Protocols for the YouTube Faces Database. 526–5268. 12 indexed citations
17.
Chang, Leonardo, et al.. (2017). Improving visual vocabularies: a more discriminative, representative and compact bag of visual words. 41(3). 2 indexed citations
18.
Chang, Leonardo, et al.. (2016). Efficient video face recognition by using Fisher Vector encoding of binary features. SPIRE - Sciences Po Institutional REpository. 1436–1441. 5 indexed citations
19.
Chang, Leonardo, et al.. (2012). FPGA-based detection of SIFT interest keypoints. Machine Vision and Applications. 24(2). 371–392. 22 indexed citations
20.
Chang, Leonardo, et al.. (2011). A Bayesian approach for object classification based on clusters of SIFT local features. Expert Systems with Applications. 39(2). 1679–1686. 23 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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