Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (3): 221-225.DOI: 10.3778/j.issn.1002-8331.1505-0091

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Sunflower seeds’ detection based on improved IVC model and gray feature

XU Can1, 2, ZHANG Qiuju1, 2   

  1. 1.School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
    2.Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi, Jiangsu 214000, China
  • Online:2017-02-01 Published:2017-05-11


徐  灿1,2,张秋菊1,2   

  1. 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

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 algorithm 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.

Key words: Active Contour Model(ACM), Image and Vision Computing(IVC), gray enhancement, local gray feature, hole defect detection

摘要: 针对IVC模型分割灰度不均匀图像时存在误分割的不足,对其进行了改进,去除了驱动力中的梯度信息且将部分因子用常数代替,不仅减少了收敛时间而且对灰度不均匀图形可实现准确的分割。针对弱光照下葵花籽孔洞不明显的特点,对其灰度进行线性增强,并用改进IVC模型分割目标;并结合局部灰度特性进行孔洞缺陷检测。实验证明,该算法能够准确地分割孔洞区域并判断该区域是否为孔洞缺陷,平均处理时约为30 ms,有较强的应用价值。

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