Developing robust algorithms for feature extraction in images of polymer layers

Sammanfattning: Automated manufacturing processes are an important component of today’s industries. Assuming the processes are properly maintained they allow for great efficiency when producing various goods. Image analysis can be used as a tool to monitor such processes and evaluate their results. This Thesis treats development of algorithms for automatic evaluation of images acquired using a standardized procedure. The images contain polymer samples from which the relative positions of two lines are extracted. These positions are thought to be related to machine settings. The image analysis algorithms have to be robust to large variation among the acquired images. Lighting and colour in general as well as shape, location and colour of the two sought lines will vary between samples. Recurring features of the images can be used as a basis of evaluation. The algorithms use several different techniques in conjunction to identify the lines. Pyramid reduction is used to reduce noise in the images and computational effort. Principal component analysis is used to reduce the number of dimensions treated from three to two. The polymer sample and one of the lines are found using thresholding and morphological operations with parameters automatically calculated using certain parts of the images. The second line is found as the path of most likely pixels considering their intensity, gradient magnitude, gradient direction and location. Finally the relative positions of the lines are examined using principal component analysis. The resulting algorithm produces relatively good results but has room for improvement, and suggestions for further work are provided. Machine settings are found to influence but not fully explain the relative positions of the two lines.