Prem Kalra

169 papers receiving 2.6k citations

Peers

Prem Kalra
Comparison fields: 5 of 155
  • Computer Vision and Pattern Recognition 1.1k
  • Computer Graphics and Computer-Aided Design 159
  • Control and Systems Engineering 839
  • Human-Computer Interaction 116
  • Signal Processing 206
Replace Chen Feng with:
Chen Feng United States
Feng Xu China
Jonathan Masci Switzerland
Nikolaos Papanikolopoulos United States
Tai‐Jiang Mu China
Alex Kendall United Kingdom
Wenli Xu China
Runsheng Xu United States
Ralph R. Martin United Kingdom
Song–Hai Zhang China
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Citations per field
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Citations per year

Countries citing papers authored by Prem Kalra

Since Specialization
Citations

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

Fields of papers citing papers by Prem Kalra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Prem Kalra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Prem Kalra Line = papers co-authored together Prem Kalra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 182 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1992197
2 1999192
3 2006165
4 2007138
5 2002124
6 199884
7 199879
8 199978
9 199569
10 201068
11 201060
12 199960
13 201148
14 201544
15 201042
16 199841
17 200438
18 201937
19
MODEL BASED FACE RECONSTRUCTION FOR ANIMATION
199935
20 200132

About Prem Kalra

Prem Kalra is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering and Signal Processing, having authored 182 papers that have together received 2.9k indexed citations. Recurring topics across this work include Neural Networks and Applications (34 papers), 3D Shape Modeling and Analysis (18 papers), Advanced Vision and Imaging (17 papers), Computer Graphics and Visualization Techniques (16 papers), Power System Optimization and Stability (14 papers), Image and Signal Denoising Methods (13 papers), Blind Source Separation Techniques (12 papers) and Human Motion and Animation (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Computer Graphics and Computer-Aided Design (159 citations), Control and Systems Engineering (839 citations), Human-Computer Interaction (116 citations) and Signal Processing (206 citations). Prem Kalra has collaborated with scholars based in India, Switzerland and United States. Frequent co-authors include Nadia Magnenat‐Thalmann, D. K. Chaturvedi, Daniël Thalmann, P.S. Satsangi, Ram Narayan Yadav, Angelo Mangili, Jisha John, K. Madhava Krishna, Subhashis Banerjee and Bipin Kumar Tripathi. Their work appears in journals such as Soft Computing, Electric Power Systems Research, Computer Graphics Forum, Applied Soft Computing and World Neurosurgery.

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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