David Crandall

10.6k total citations · 2 hit papers
134 papers, 5.4k citations indexed

About

David Crandall is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction. According to data from OpenAlex, David Crandall has authored 134 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Computer Vision and Pattern Recognition, 31 papers in Artificial Intelligence and 17 papers in Human-Computer Interaction. Recurrent topics in David Crandall's work include Advanced Image and Video Retrieval Techniques (31 papers), Video Surveillance and Tracking Methods (20 papers) and Visual Attention and Saliency Detection (17 papers). David Crandall is often cited by papers focused on Advanced Image and Video Retrieval Techniques (31 papers), Video Surveillance and Tracking Methods (20 papers) and Visual Attention and Saliency Detection (17 papers). David Crandall collaborates with scholars based in United States, China and Israel. David Crandall's co-authors include Daniel P. Huttenlocher, Jon Kleinberg, Lars Bäckström, Wenguan Wang, Siddharth Suri, Dan Cosley, Apu Kapadia, Xiankai Lu, D.P. Huttenlocher and Noah Snavely and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nano Letters and ACS Nano.

In The Last Decade

David Crandall

129 papers receiving 5.2k 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.

Mapping the world's photos

2009 552 citations David Crandall et al. profile →
A Survey on Deep Learning Technique for Video Segmentation

2022 159 citations David Crandall et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Crandall United States	34	2.8k	1.1k	653	502	498	134	5.4k
Thomas Ertl Germany	43	5.3k 1.9×	1.0k 0.9×	350 0.5×	340 0.7×	141 0.3×	469	8.8k
Tony Jebara United States	33	2.1k 0.8×	2.3k 2.1×	812 1.2×	384 0.8×	176 0.4×	103	6.4k
Nuria Oliver Spain	37	4.0k 1.4×	1.9k 1.7×	660 1.0×	762 1.5×	136 0.3×	138	8.2k
Qin Lv United States	36	1.0k 0.4×	1.2k 1.1×	424 0.6×	366 0.7×	268 0.5×	144	5.3k
Pan Hui Hong Kong	51	1.5k 0.5×	1.4k 1.3×	576 0.9×	1.6k 3.2×	266 0.5×	388	13.8k
Daniel Gática-Pérez Switzerland	52	3.6k 1.3×	2.6k 2.4×	899 1.4×	1.4k 2.8×	122 0.2×	282	9.2k
Alan F. Smeaton Ireland	44	4.1k 1.4×	2.4k 2.2×	867 1.3×	174 0.3×	173 0.3×	473	8.6k
Bo Liu China	34	1.1k 0.4×	1.5k 1.4×	317 0.5×	206 0.4×	192 0.4×	408	4.6k
Lei Li China	35	2.0k 0.7×	2.8k 2.6×	225 0.3×	143 0.3×	242 0.5×	284	6.0k
John Krumm United States	34	2.5k 0.9×	1.0k 0.9×	432 0.7×	1.5k 2.9×	436 0.9×	112	6.5k

Countries citing papers authored by David Crandall

Since Specialization

Citations

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

Fields of papers citing papers by David Crandall

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Crandall

This figure shows the co-authorship network connecting the top 25 collaborators of David Crandall. A scholar is included among the top collaborators of David Crandall 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 David Crandall. David Crandall 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.

Joshi, Swapna, Natasha Randall, Weslie Khoo, et al.. (2025). Let's make up a story: design and evaluation of an improvisational storytelling robot to foster older adults' meaning and purpose in life. PubMed. 4. 1691140–1691140.

2.

Losovyj, Yaroslav, et al.. (2025). Stable versus Temporally Sensitive Optical Security Tags from Metal Nanoparticles. Nano Letters. 25(12). 4781–4789. 1 indexed citations

3.

Khoo, Weslie, et al.. (2025). Bittersweet Snapshots of Life: Designing to Address Complex Emotions in a Reminiscence Interaction between Older Adults and a Robot. 1–18. 3 indexed citations

4.

Khoo, Weslie, et al.. (2025). Research as Care: A Reflection on Incorporating the Ethics of Care in Design Research with People Living with Dementia. 3013–3027.

5.

Crandall, David, et al.. (2024). GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo. 3230–3240. 15 indexed citations

6.

Tsui, Katherine M., et al.. (2024). If [YourName] Can Code, So Can You! End-User Robot Programming For Non-Experts. 1033–1037. 3 indexed citations

7.

Stafford, Philip B., et al.. (2024). "Give it Time:" Longitudinal Panels Scaffold Older Adults' Learning and Robot Co-Design. 283–292. 6 indexed citations

8.

Khoo, Weslie, et al.. (2024). Is Now a Good Time? Opportune Moments for Interacting with an Ikigai Support Robot. 549–553. 2 indexed citations

9.

Crandall, David, et al.. (2024). Modular Anti‐Counterfeit Tags Formed by Template‐Assisted Self‐Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning. Advanced Functional Materials. 34(41). 11 indexed citations

10.

Khoo, Weslie, et al.. (2023). Spill the Tea. 178–182. 17 indexed citations

11.

Amatuni, Andrei, et al.. (2021). In-the-Moment Visual Information from the Infant's Egocentric View Determines the Success of Infant Word Learning: A Computational Study. eScholarship (California Digital Library). 3 indexed citations

12.

Smith, Joshua D., et al.. (2021). Plasmonic Anticounterfeit Tags with High Encoding Capacity Rapidly Authenticated with Deep Machine Learning. ACS Nano. 15(2). 2901–2910. 72 indexed citations

13.

Leake, David, et al.. (2021). Supporting Case-Based Reasoning with Neural Networks: An Illustration for Case Adaptation.. 10 indexed citations

14.

Xiao, Qingyang, et al.. (2020). Pose-Guided Knowledge Transfer for Object Part Segmentation. 3961–3965. 6 indexed citations

15.

Tsutsui, Satoshi, et al.. (2020). A Computational Model of Early Word Learning from the Infant's Point of View.. eScholarship (California Digital Library). 1 indexed citations

16.

Chen, Zhenhua, et al.. (2020). Deep Neural Network–Based Detection and Verification of Microelectronic Images. 4(1). 44–54. 20 indexed citations

17.

Crandall, David, et al.. (2020). Automatic Dense Annotation for Monocular 3D Scene Understanding. IEEE Access. 8. 68852–68865. 1 indexed citations

18.

Crandall, David, et al.. (2018). From Coarse Attention to Fine-Grained Gaze: A Two-stage 3D Fully Convolutional Network for Predicting Eye Gaze in First Person Video.. British Machine Vision Conference. 295. 4 indexed citations

19.

Ahmed, Tousif, et al.. (2016). Addressing Physical Safety, Security, and Privacy for People with Visual Impairments. Symposium On Usable Privacy and Security. 341–354. 20 indexed citations

20.

Bambach, Sven, David Crandall, Linda B. Smith, & Yu Chen. (2016). Active Viewing in Toddlers Facilitates Visual Object Learning: An Egocentric Vision Approach.. Cognitive Science. 12 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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