.png){kind=icon}
Return
Binary plankton image classification
.png){kind=icon}
PRE
AI
DOI：10.1109/JOE.2004.836995.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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, One 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.
Keywords:
binary plankton images
feature extraction
principal component analysis (PCA)
two-dimensional (2-D) shape recognition
Journal
IF:
5.3
Papers:
2.6K
Citations:
7.4K
Organization
No organization information available
Cited Papers
Lipid peroxidation and superoxide dismutase activity in relation to photoinhibition induced by chilling in moderate light.png)
Planta
PlantaIF0
PREAI
ShareSave
ShareSaveShareSaveTesting Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI PlatformForests
OAAI
ForestsIF0
OAAIShareSaveShareSaveShareSave
View more