No-Reference Quality Assessment of 3D Prints Based on the GLCM Analysis

Quality assessment of 3D prints is one of the newest challenges for machine vision. As the 3D printing is relatively new technology, it is still far from perfection and there are many static or dynamically changing factors which can affect the quality of the final 3D prints. Considering the relatively long time necessary for printing, it may be reasonable to interrupt the printing process in order to save the filament (or another material) and time in the case of decreased quality of already printed fragment of an object. Such monitoring requires the use of the video feedback with appropriate image analysis methods which should allow a reliable quality assessment of the printed object's part. Typically such assessment should not be based on the comparison with a reference image, as in many image quality assessment methods, because such an image is usually unavailable. Therefore in this paper an approach for the noreference quality assessment of the 3D prints based on the analysis of the Gray-Level Co-occurrence Matrix (GLCM) and chosen Haralick features is proposed and investigated. Obtained experimental results demonstrate the validity and usefulness of the proposed approach.