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Automated keypad inspection by image registration using hough transform based perimeter extraction and multilevel pyramids

机译:使用基于霍夫变换的周边提取和多层金字塔通过图像配准来自动进行键盘检查

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摘要

Manufacturing and Quality inspection using Machine Vision is diversely increasing in various processes and in various industries. However there are various aspects which need to be considered in order to provide the best quality output like illumination conditions, alignment of objects, efficiency of the machine, time required to detect the flaws etc. There exists a need of a robust and autonomous system which will inspect the keypad labels for quality and discard the defective samples. The sample (keypad label in this case) is taken by the user and given to a machine which automatically inspects and detects the flaw present in the sample. To identify and classify a faulty area of an image, image subtraction is carried out which is possible only when the two objects are spatially aligned. Hence the process of Image Registration (IR) is demanded. The ultimate goal of this paper is to register the multi temporal images in order to minimize the misalignment and compute a high correlation factor by Hough Transform based Perimeter Extraction (HTPE) and Image Registration using Multilevel Pyramids (IRMP). The processes of defect inspection are computationally simple, fast and yield 90% of result accuracy.
机译:使用Machine Vision的制造和质量检查在各个过程和各个行业中以不同的方式增长。然而,为了提供最佳质量的输出,例如照明条件,物体的对准,机器的效率,检测缺陷所需的时间等,需要考虑各个方面。将检查键盘标签的质量,并丢弃有缺陷的样品。样品(在这种情况下为键盘标签)由用户获取,并提供给自动检查并检测样品中存在缺陷的机器。为了识别和分类图像的故障区域,进行图像相减,这仅在两个对象在空间上对齐时才可能。因此,需要图像配准(IR)的过程。本文的最终目标是通过基于霍夫变换的周界提取(HTPE)和使用多级金字塔(IRMP)的图像配准,对多时间图像进行配准,以最大程度地减少失准并计算高相关因子。缺陷检查的过程计算简单,快速,并且可以产生90%的结果精度。

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