The traditional method of line laser stripes extraction is to binarize the image taken by cameras with a threshold value, but the size of laser lines in different color areas are obviously different because the line laser is absorbed by different colors of the parts to be measured to different degrees. For some regions with similar color to the laser, the traditional laser fringe recognition method cannot even identify the laser line, which results in the serious vacancy of the points cloud obtained by linear structured light scanning in some regions. An adaptive threshold method for laser fringe extraction is proposed in this paper. A modified HSV space is firstly proposed and the characteristics of the laser stripes on different colors of cardboard based on the modified HSV space is analyzed. Then a new image with high contrast through the quantized characteristics of the stripes is synthesized; Finally, the neighborhood filtering method is used to perform filtering and the Steger method is employed to extract the center of the laser stripe. Experimental results show that the method proposed in this paper can better segment the laser fringe from the background than the traditional algorithm, which lays a good foundation for the calculation of laser fringe center and hence to improve the measuring capability of laser structured light.
Additive Manufacturing (AM) technology is considered as one of the most promising manufacturing technologies in the areas of aerospace and defense industries. However, the AM parts are known to have relatively high residual stresses and a variety of defects, such as porosity, balling, cracking, etc. Therefore, it is critically important to monitor the quality of products during the AM manufacturing process. In this paper, we proposed a novel enhanced fusion algorithm based on Finite Discrete Shearlet Transform (FDST) and multi-scale sequential toggle operator (MSSTO) for visible and infrared images fusion in the AM systems. The original images can be decomposed into low-frequency and high-frequency subband images by FDST. Then, the effective bright and dark image informations are extracted from the low frequency coefficients of source images by MSSTO transform, which are injected into the fusing low frequency coefficient to obtain the final low frequency synthetic coefficient. The high frequency sub-band coefficients are fused by using the local spatial frequency weighting and region energy. The fused image can be obtained by the FDST inverse transformation of the high and low fused coefficient. Experiments show that the proposed algorithm can get more texture information while retaining the significant features of the images, acheiving good detection and indetification results of the defects properties.
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.