Feature extraction for animal fiber identification
| Autor: | Lingxue Kong, Saeid Nahavandi, F. H. She, Abbas Z. Kouzani |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Artificial neural network
business.industry Fiber (mathematics) Computer science Feature extraction Pattern recognition Image processing Image segmentation Machine learning computer.software_genre Hybrid system Principal component analysis Identification (biology) Artificial intelligence business computer |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.477055 |
| Popis: | Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones. |
| Databáze: | OpenAIRE |
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