Rakesh Mehta

2.3k citations
11 papers · 510 indexed · h-index 7
Topics
Image Retrieval and Classification Techniques (6 papers)Advanced Image and Video Retrieval Techniques (5 papers)Medical Image Segmentation Techniques (3 papers)
Partner nations
IrelandFinlandIndia

In The Last Decade

Rakesh Mehta

11 papers receiving 479 citations

Peers

Rakesh Mehta
Comparison fields: 5 of 96
  • Computer Vision and Pattern Recognition 249
  • Industrial and Manufacturing Engineering 163
  • Media Technology 55
  • Biomedical Engineering 51
  • Artificial Intelligence 47
Replace Sergey Ivanov with:
Sergey Ivanov Russia
Maurice Pillet France
Nagul Cooharojananone Thailand
Maryam Mousavi Malaysia
Arvind Kalia India
Grahame Cooper United Kingdom
Md Ileas Pramanik Bangladesh
Xiaotong Wu China
Anand Singh Rajawat India
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Rakesh Mehta relative to Sergey Ivanov Russia Sergey Ivanov's profile →
Citations per field
00.5×10×17×
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Citations per year

Countries citing papers authored by Rakesh Mehta

Since Specialization
Citations

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

Fields of papers citing papers by Rakesh Mehta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rakesh Mehta

This figure shows the co-authorship network connecting the top 25 collaborators of Rakesh Mehta. A scholar is included among the top collaborators of Rakesh Mehta 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 Rakesh Mehta. Rakesh Mehta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
#WorkIndexed citations
1 205
2 13
3 1
4 5
5 39
6 137
7 28
8 37
9 3
10 39
11 3

About Rakesh Mehta

Rakesh Mehta is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Media Technology, having authored 11 papers that have together received 510 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (6 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Medical Image Segmentation Techniques (3 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (163 citations), Computer Vision and Pattern Recognition (249 citations) and Media Technology (55 citations). Rakesh Mehta has collaborated with scholars based in Ireland, Finland and India. Frequent co-authors include Karen Egiazarian, Timo Kuula, Päivi Heikkilä, Susanna Aromaa, S Mach, Marja Liinasuo, Franziska Schmalfuß, Juhani Heilala, Eija Kaasinen and Menouer Boubekeur. Their work appears in journals such as IEEE Transactions on Image Processing, Pattern Recognition and Pattern Recognition Letters.

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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