Large scale stencil images used for surface mount technology (SMT) always have more than ten thousand closed graphics(stencil holes). It is difficult to find corresponding information from those graphics in stencil image registration. Here, we propose a novel method which is based on two-node tree, differed from traditional ones. The two-node tree is special, which has only two nodes in a layer. It functions as selecting feature points. The set of feature points with the erroneous can find the most reasonable projection transformation model by the simplified RANSAC algorithm. We adopt different types of defective stencil images to verify the proposed method. Experimental results fully show its robustness and high-tolerant rate.
Since the stencil image used for surface mount technology (SMT) always has various defects such as less holes and burrs in the laser processing and imaging, it is indispensable to detect those flaws with high accuracy. An automatic registration lies at the root of identifying defects. In this paper, a novel automatic registration algorithm for stencil images is proposed. According to the distribution probability density of the coordinates of gravity center points in a stencil image, the adaptive parameter DBSCAN clustering algorithm is adopted to classify those points. As a result, we could find corresponding gravity center points (feature points) in the stencil image and its standard design file respectively. A transformation matrix between the stencil image and its standard design file is obtained by the feature points. Experiments have shown that this automatic registration algorithm can be well adapted to the stencil images with random defects.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.