This work presents two image-based inspection approaches for the quality evaluation of cylinder bore surfaces. In the first algorithm, metal folds on plateau-honed surfaces are inspected with scanning electron microscopy. An edge-aware structure tensor is proposed for feature extraction and localization of surface defects. The second algorithm uses a morphgraphical method for detecting graphite grains in optical micrographs. Based on the inspection results, quality parameters are proposed.

This work presents two image-based inspection approaches for the quality evaluation of cylinder bore surfaces. In the first algorithm, metal folds on plateau-honed surfaces are inspected with scanning electron microscopy. An edge-aware structure tensor is proposed for feature extraction and localization of surface defects. The second algorithm uses a morphgraphical method for detecting graphite grains in optical micrographs. Based on the inspection results, quality parameters are proposed. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

The automatic retrieval of images according to the similarity of their content is a challenging task with many application fields. In this book the automatic retrieval of images according to human spontaneous perception without further effort or knowledge is considered. A system is therefore designed and analyzed. Methods for the detection and extraction of regions and for the extraction and comparison of color, shape, and texture features are also investigated.

Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.

While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials' spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results.

Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.

This work presents model-based algorithmic approaches for interference-invariant time delay estimation, which are specifically suited for the estimation of small time delay differences with a necessary resolution well below the sampling time. Therefore, the methods can be applied particularly well for transit-time ultrasonic flow measurements, since the problem of interfering signals is especially prominent in this application.