.png){kind=icon}
返回
3-D Granulometry Using Image Processing
.png){kind=icon}
OA
AI
DOI：10.1109/TII.2018.2856466.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image-based methods for estimating the particle size distribution (granulometry) usually analyze two-dimensional (2-D) samples of particles disposed on a conveyor belt. Such approaches have to deal with occlusions and cannot evaluate the thickness of each particle. Three-dimensional (3-D) vision systems can reduce the acquisition constraints and speed up the quality control process. This paper proposes a novel 3-D vision system for analyzing the granulometry of falling particles. The system is designed to work in real time and to compute a partial 3-D reconstruction of the particle from a single pair of two-view images, which is then enhanced by using a neural-based technique. The validation of the proposed approach has been performed by considering three application scenarios for which the system achieved satisfactory accuracy and robustness.
Keyword:
Falling particles
granulometry
image processing
3-D reconstruction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.9
论文数:
8.3K
被引数:
6.0W
机构
引用论文
Characterization of bio-dynamic speckles through classical and fuzzy mathematical morphology tools.png)
SIGNAL PROCESSINGOAAI
SIGNAL PROCESSINGIF3.6
OAAI分享收藏分享收藏分享收藏
查看更多内容