Publication2025
Using Multilevel-Sparse Autoencoder-Based Robust Features for Estimation of Large Homography
- Industry, Innovation and Infrastructure
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- Ocit 2025 Proceedings 23rd Oits International Conference on Information Technology
- Pages
- 846-852
- Published
- 1 January 2025
- DOI
- 10.1109/ocit66168.2025.11400183
- Scopus ID
- 105036742163
- Authors
- 4
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Sparse autoencoder features for large homography estimation in earth and space exploration.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3 Innovation, Science and Technology
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 9 label. Sparse autoencoder features for large homography estimation in earth and space exploration.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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KIIT Deemed to be University. “Using Multilevel-Sparse Autoencoder-Based Robust Features for Estimation of Large Homography”. Kortex.