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Robustness Improvement of Visual Templates Matching Based on Frequency-Tuned Model in RatSLAM
This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546858/ https://www.ncbi.nlm.nih.gov/pubmed/33101002 http://dx.doi.org/10.3389/fnbot.2020.568091 |