计算机工程与应用2017，Vol.53Issue(3)：221-225,5.DOI:10.3778/j.issn.1002-8331.1505-0091
基于改进IVC模型和灰度特性的葵花籽缺陷检测
Sunflower seeds'detection based on improved IVC model and gray feature
徐灿 1张秋菊2
作者信息
- 1. 江南大学 机械工程学院,江苏 无锡 214122
- 2. 江苏省食品先进制造装备技术重点实验室,江苏 无锡 214000
- 折叠
摘要
Abstract
Considering the incorrect segmentation faults of IVC model segmenting the gray uneven images, this paper proposes an improved algorithm. It removes the gradient information and replaces some factors with constant, which not only decreases the convergence time, but also can segment the gray uneven images accurately. In view of the sunflower seeds'holes not obvious in the weak light, it enhances its gray linearly and uses the improved IVC model to segment goals, and then combines with local gray characteristics to detect hole defections. The results demonstrate that this algo-rithm can segment the holes accurately and determine whether its area is hole defects and its average process time is about 30 ms, so it has strong application value.关键词
主动轮廓模型(ACM)/图像与视觉计算(IVC)/灰度增强/局部灰度特性/孔洞缺陷检测Key words
Active Contour Model(ACM)/Image and Vision Computing(IVC)/gray enhancement/local gray feature/hole defect detection分类
信息技术与安全科学引用本文复制引用
徐灿,张秋菊..基于改进IVC模型和灰度特性的葵花籽缺陷检测[J].计算机工程与应用,2017,53(3):221-225,5.
