Pablo Arbelaez: h-index, Total Citations, and Citation Map
Pablo Arbelaez's h-index is 56 (108 i10-index, 38,353+ total citations across 197+ publications) according to Google Scholar as of September 2026. Pablo Arbelaez is affiliated with Universidad de los Andes, Bogota, Colombia.
Pablo Arbelaez is a researcher affiliated with Universidad de los Andes, Bogota, Colombia, specializing in Computer Vision, Artificial Intelligence, Machine Learning. Their work has been cited 38,353 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Pablo Arbelaez's Citation Metrics
Bibliometric impact based on 197 indexed publications.
- H-Index
- 56
- i10-Index
- 108
- Total Citations
- 38,353
- Citing Countries
- 69
As of September 2026.
Pablo Arbelaez has an h-index of 56 and 38,353 total citations across 197 publications, with research cited by institutions in 69 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Pablo Arbelaez's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Contour detection and hierarchical image segmentation
20107,265
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
18 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a foundational framework for hierarchical image segmentation using ultrametric contour maps, establishing a scalable approach to boundary extraction that significantly advanced computer vision methodologies.
The researcher introduced a foundational method for deriving semantic contours from inverse detectors, establishing a highly cited framework for object representation in computer vision.
The researcher developed a multiscale combinatorial grouping method for image segmentation and object proposal generation, establishing a foundational approach widely adopted by the computer vision community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Pablo Arbelaez's research
Pablo Arbelaez is a researcher in Computer Vision, Artificial Intelligence and Machine Learning at Universidad de los Andes, Bogota, Colombia. Their work has been cited 38,353 times across 197 publications (h-index 56), according to Google Scholar.
Their most-cited work, “Contour detection and hierarchical image segmentation” (2010), has accumulated 7,265 citations. Other influential works include “Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge” (2018) with 2,909 citations and “Semantic contours from inverse detectors” (2011) with 2,277 citations.
Citations of Pablo Arbelaez's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
