Automatic Classification of Hemp and Cotton in Digital Macro Photography using VGG-16 for Nondestructive Paper Analysis
| Autor: | Yoichi Ohyanagi, Akiko Iwata, Mikako Suzuki, Yuri Urano, Ayu Ikuta, Ami Oshima, Koji Shibazaki, Naoki Kamiya |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
040101 forestry
business.product_category Computer science business.industry Photography 04 agricultural and veterinary sciences 010501 environmental sciences 01 natural sciences Digital image Data acquisition 0401 agriculture forestry and fisheries Computer vision Artificial intelligence business Macro photography 0105 earth and related environmental sciences Digital camera |
| Zdroj: | GCCE |
| DOI: | 10.1109/gcce46687.2019.9015416 |
| Popis: | The Elucidation of the spread of paper is an important research that reveals historical culture. In particular, clarifying the paper fibers contained in paper is one of the important issues involved in elucidating the propagation of paper. The target of our research is an image of the Samarkand paper acquired from historical cultural properties. In this research, images of the paper were acquired by macro photography with a commercially available digital camera that can obtain samples nondestructively. By using the VGG-16, which is a deep convolutional neural network (DCNN), we easily determined whether the paper from which the macro image was obtained has a hemp or cotton fiber. In addition, as the piece of paper to be photographed is a cultural property, data acquisition through simple photography is required. However, one macro shot with a digital camera causes blurring at the periphery of the image. Therefore, the DCNN-based classification performance was compared using two datasets of the input image and the image obtained by trimming the periphery. As a result of trimming of the image periphery prone to blur in macro photography of digital images, classification results of up to 95.8% were obtained. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|