Binary Plankton Image Classification
| Autor: | Scott Samson, Andrew Remsen, Xiaoou Tang, Feng Lin |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Contextual image classification
business.industry Computer science Mechanical Engineering fungi Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Binary number Ocean Engineering Image processing Pattern recognition Plankton Image (mathematics) Principal component analysis Computer vision Artificial intelligence Electrical and Electronic Engineering Underwater business |
| Zdroj: | IEEE Journal of Oceanic Engineering. 31:728-735 |
| ISSN: | 0364-9059 |
| DOI: | 10.1109/joe.2004.836995 |
| Popis: | In marine biology study, it is important to investigate the distribution of plankton organisms. Because of the overwhelming data size, automatic processing of the large amount of image data collected by underwater image recorders becomes inevitable. However, due to the fragmentation and the large within-class variations of binary plankton images, it is difficult to extract reliable shape features. In this paper, we propose several new shape descriptors and use a normalized multilevel dominant eigenvector estimation method to select a best feature set for binary plankton image classification. We achieve more than 91% classification accuracy in experiments on more than 3000 images |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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