| Autor: |
Noah Gardner, John Paul Hellenbrand, Anthony Phan, Haige Zhu, Zhiling Long, Min Wang, Clint A. Penick, Chih-Cheng Hung |
| Rok vydání: |
2022 |
| Zdroj: |
Applied Computing and Intelligence. 2:133-151 |
| ISSN: |
2771-392X |
| Popis: |
Ant cuticle texture presumably provides some type of function, and therefore is useful to research for ecological applications and bioinspired designs. In this study, we employ statistical image texture analysis and deep machine learning methods to classify similar ant species based on morphological features. We establish a public database of ant cuticle images for research. We provide a comparative study of the performance of image texture classification and deep machine learning methods on this ant cuticle dataset. Our results show that the deep learning methods give higher accuracy than statistical methods in recognizing ant cuticle textures. Our experiments also reveal that the deep learning networks designed for image texture performs better than the general deep learning networks. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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