Amir Saffari

53 papers receiving 3.8k citations

Hit Papers

Struck: Structured output tracking with kernels20112026201620212011201520204008001.2k

Peers

Amir Saffari
Comparison fields: 5 of 161
  • Computer Vision and Pattern Recognition 2.9k
  • Artificial Intelligence 718
  • Aerospace Engineering 625
  • Safety, Risk, Reliability and Quality 383
  • Electrical and Electronic Engineering 320
Replace Xin Zhao with:
Xin Zhao China
Moulay A. Akhloufi Canada
Sangdoo Yun South Korea
Matej Kristan Slovenia
Lijun Wang China
Zhun Zhong China
Bohyung Han South Korea
Xiangyuan Lan China
Toby P. Breckon United Kingdom
Youngjoon Yoo South Korea
Amir Saffari relative to Xin Zhao China Xin Zhao's profile →
Citations per field
00.5×7.8×
Xin Zhao · 1×
Citations per year

Countries citing papers authored by Amir Saffari

Since Specialization
Citations

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

Fields of papers citing papers by Amir Saffari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amir Saffari

This figure shows the co-authorship network connecting the top 25 collaborators of Amir Saffari. A scholar is included among the top collaborators of Amir Saffari 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 Amir Saffari. Amir Saffari 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
#WorkIndexed citations
1 23
2 29
3 11
4 1
5 1
6
Applications of machine learning to diagnosis and treatment of neurodegenerative diseasesbreakdown →
350
7 1
8 7
9 3
10 1
11
Evaluation of the effect of macerals on coal permeability in Tazareh and Parvadeh mines
4
12 11
13
Applying Rock Engineering Systems (RES) approach to Evaluate and Classify the Coal Spontaneous Combustion Potential in Eastern Alborz Coal Mines
22
14 42
15
Learning Anchor Planes for Classification
17
16
Model Selection: Beyond the Bayesian/Frequentist Divide
93
17 335
18 21
19 16
20 0

About Amir Saffari

Amir Saffari is a scholar working on Fuel Technology, Computer Vision and Pattern Recognition and Ocean Engineering, having authored 56 papers that have together received 3.9k indexed citations. Recurring topics across this work include Coal Properties and Utilization (12 papers), Video Surveillance and Tracking Methods (9 papers) and Topic Modeling (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.9k citations), Safety, Risk, Reliability and Quality (383 citations) and Aerospace Engineering (625 citations). Amir Saffari has collaborated with scholars based in Iran, Austria and Germany. Frequent co-authors include Philip H. S. Torr, Sam Hare, Horst Bischof, Christian Leistner, Stuart Golodetz, Ming‐Ming Cheng, Stephen L. Hicks, Vibhav Vineet, Martin Godec and Thomas Pock. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Fluid Mechanics.

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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