Automate fry counting using computer vision and multi-class least squares support vector machine
文献类型：期刊论文
| 作者 | Fan, Liangzhong1; Liu, Ying2 ; Liu, Y
|
| 刊名 | AQUACULTURE
 |
| 出版日期 | 2013-03-04 |
| 卷号 | 380页码:91-98 |
| 关键词 | Back propagation neural network Least squares support vector machine Computer vision Fry counting |
| ISSN号 | 0044-8486 |
| 通讯作者 | Liu, Y |
| 中文摘要 | In this paper, an approach based on geometric features to count overlapping fry fish is presented. Back propagation neural network (BPNN) and least squares support vector machine (LS-SVM) were used to construct classification models. 19 video clips with fish numbers varying from 10 to 100 were captured by a computer vision system. A total of 600 sub-images with overlapping fish were randomly selected, 300 images were used as a training set to create a calibration model, and remaining images were used to verify the model. 7 geometric features (area, perimeter, convex area, bounding box width, bounding box height, skeleton length, endpoint number) were obtained from the overlapping fish images. Results indicate that the best performance with about 98.73% of the average counting accuracy rate is achieved by LS-SVM model, which is better than the performance of BPNN model. The combined multiple geometric features coupled with an LS-SVM classifier is a highly accurate way for fry fish counting. (C) 2012 Published by Elsevier B.V. |
| 英文摘要 | In this paper, an approach based on geometric features to count overlapping fry fish is presented. Back propagation neural network (BPNN) and least squares support vector machine (LS-SVM) were used to construct classification models. 19 video clips with fish numbers varying from 10 to 100 were captured by a computer vision system. A total of 600 sub-images with overlapping fish were randomly selected, 300 images were used as a training set to create a calibration model, and remaining images were used to verify the model. 7 geometric features (area, perimeter, convex area, bounding box width, bounding box height, skeleton length, endpoint number) were obtained from the overlapping fish images. Results indicate that the best performance with about 98.73% of the average counting accuracy rate is achieved by LS-SVM model, which is better than the performance of BPNN model. The combined multiple geometric features coupled with an LS-SVM classifier is a highly accurate way for fry fish counting. (C) 2012 Published by Elsevier B.V. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 学科主题 | Fisheries ; Marine & Freshwater Biology |
| 类目[WOS] | Fisheries ; Marine & Freshwater Biology |
| 研究领域[WOS] | Fisheries ; Marine & Freshwater Biology |
| 关键词[WOS] | FISH COUNTER ; CLASSIFICATION ; SALMON |
| 收录类别 | SCI |
| 原文出处 | 10.1016/j.aquaculture.2012.10.016 |
| 语种 | 英语 |
| WOS记录号 | WOS:000314642900015 |
| 公开日期 | 2014-07-17 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/16460]  |
| 专题 | 海洋研究所_海洋生态与环境科学重点实验室 海洋研究所_海洋生物技术研发中心 |
| 通讯作者 | Liu, Y |
| 作者单位 | 1.Zhejiang Univ, Ningbo Inst Technol, Ningbo 315100, Zhejiang, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fan, Liangzhong,Liu, Ying,Liu, Y. Automate fry counting using computer vision and multi-class least squares support vector machine[J]. AQUACULTURE,2013,380:91-98. |
| APA | Fan, Liangzhong,Liu, Ying,&Liu, Y.(2013).Automate fry counting using computer vision and multi-class least squares support vector machine.AQUACULTURE,380,91-98. |
| MLA | Fan, Liangzhong,et al."Automate fry counting using computer vision and multi-class least squares support vector machine".AQUACULTURE 380(2013):91-98. |
入库方式： OAI收割
来源：海洋研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。