PDF(Pubmed)
Abstract：
Machine vision is a desirable non-contact measurement method for hot forgings, as image segmentation has been a challenging issue in performance and robustness resulting from the diversity of working conditions for hot forgings. Thus, this paper proposes an efficient and robust active contour model and corresponding image segmentation approach for forging images, by which verification experiments are conducted to prove the performance of the segmentation method by measuring geometric parameters for forging parts. Specifically, three types of continuity parameters are defined based on the geometric continuity of equivalent grayscale surfaces for forging images; hence, a new image force and external energy functional are proposed to form a new active contour model, Geometric Continuity Snakes (GC Snakes), which is more percipient to the grayscale distribution characteristics of forging images to improve the convergence for active contour robustly; additionally, a generating strategy for initial control points for GC Snakes is proposed to compose an efficient and robust image segmentation approach. The experimental results show that the proposed GC Snakes has better segmentation performance compared with existing active contour models for forging images of different temperatures and sizes, which provides better performance and efficiency in geometric parameter measurement for hot forgings. The maximum positioning and dimension errors by GC Snakes are 0.5525 mm and 0.3868 mm, respectively, compared with errors of 0.7873 mm and 0.6868 mm by the Snakes model.
摘要：
机器视觉是热锻件理想的非接触测量方法，由于热锻件工作条件的多样性，图像分割在性能和鲁棒性方面一直是一个具有挑战性的问题。因此，本文针对锻造图像提出了一种高效、鲁棒的活动轮廓模型和相应的图像分割方法,通过测量锻件的几何参数，进行了验证实验，以证明分割方法的性能。具体来说,基于用于锻造图像的等效灰度表面的几何连续性，定义了三种类型的连续性参数；因此，提出了一种新的图像力和外部能量函数来形成新的主动轮廓模型，几何连续性蛇（GC蛇），这更适合于锻造图像的灰度分布特征，以鲁棒地提高主动轮廓的收敛性；此外，提出了一种GCSnakes初始控制点的生成策略，以构成一种高效，鲁棒的图像分割方法。实验结果表明，对于不同温度和大小的锻造图像，与现有的活动轮廓模型相比，提出的GCSnakes具有更好的分割性能，为热锻件的几何参数测量提供了更好的性能和效率。GCSnakes的最大定位和尺寸误差为0.5525mm和0.3868mm，分别,与Snakes模型的0.7873mm和0.6868mm的误差相比。
