Amit Saxena
- Computer Vision and Pattern Recognition top 0.5%
- Media Technology top 0.5%
- Artificial Intelligence top 5%
- Aerospace Engineering top 5%
- Signal Processing top 5%
- Co-authors
- A.Y. NgMin SunMukesh PrasadChin‐Teng LinAruna TiwariAkshansh GuptaOm Prakash PatelWeiping Ding
- Topics
- Face and Expression Recognition (17 papers)Neural Networks and Applications (8 papers)Evolutionary Algorithms and Applications (5 papers)
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyComputer Graphics and Computer-Aided Design
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceNeurocomputingApplied Sciences
- Partner nations
- IndiaAustraliaUnited States
In The Last Decade
Amit Saxena
32 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Computer Vision and Pattern Recognition 1.3k
- Media Technology 538
- Artificial Intelligence 456
- Aerospace Engineering 258
- Signal Processing 109
Countries citing papers authored by Amit Saxena
This map shows the geographic impact of Amit Saxena'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 Amit Saxena with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amit Saxena more than expected).
Fields of papers citing papers by Amit Saxena
This network shows the impact of papers produced by Amit Saxena. 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 Amit Saxena. The network helps show where Amit Saxena may publish in the future.
Co-authorship network of co-authors of Amit Saxena
This figure shows the co-authorship network connecting the top 25 collaborators of Amit Saxena. A scholar is included among the top collaborators of Amit Saxena 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 Amit Saxena. Amit Saxena is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 4 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 2 | |
| 11 | 6 | |
| 12 | Filter GA Based Approach to Feature Selection for Classification | 5 |
| 13 | 7 | |
| 14 | 4 | |
| 15 | 7 | |
| 16 | 1 | |
| 17 | 11 | |
| 18 | 1 | |
| 19 | 2 | |
| 20 | Make3D: Learning 3D Scene Structure from a Single Still Imagebreakdown → | 1092 |
About Amit Saxena
Amit Saxena is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 40 papers that have together received 2.1k indexed citations. Recurring topics across this work include Face and Expression Recognition (17 papers), Neural Networks and Applications (8 papers) and Evolutionary Algorithms and Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Media Technology (538 citations) and Computer Graphics and Computer-Aided Design (72 citations). Amit Saxena has collaborated with scholars based in India, Australia and United States. Frequent co-authors include A.Y. Ng, Min Sun, Mukesh Prasad, Chin‐Teng Lin, Aruna Tiwari, Akshansh Gupta, Om Prakash Patel, Weiping Ding, Neha Bharill and Meng Joo Er. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neurocomputing and Applied Sciences.
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.