Scanning electron microscopes (SEM) are widely used to analyse the morphology of all kind of specimen providing high resolution image data. To overcome the two-dimensional limitation, a lot of effort has been put into the recovery of the hidden third dimension based on the acquired SEM images throughout the last decades. Especially methods based on photogrammetry were identified to yield qualitatively good reconstruction results. Nevertheless, precise quantitative 3D measurements still remain a challenge. One of the key problems is the robust estimation of the motion in the acquired image sequences. A possible solution is given by the factorization method for orthographic image streams. To evaluate the applicability of this algorithm on SEM image sequences, the motion for several sequences is estimated and compared to the stage settings. Furthermore, the method is extended to obtain a dense reconstruction from a stereo-pair based on the estimated rotations between the two views. The final reconstruction results are compared to reference measurements with a confocal laser scanning microscope for a quantitative evaluation.
KEYWORDS: Scanning electron microscopy, 3D image processing, Clouds, Particles, 3D modeling, Reconstruction algorithms, 3D image reconstruction, Electron microscopes
Scanning electron microscopes (SEM) allow a detailed surface analysis of a wide variety of specimen. However, SEM image data does not provide depth information about a captured scene. This limitation can be overcome by recovering the hidden third dimension of the acquired SEM micrographs, for instance to fully characterize a particle agglomerate’s morphology. In this paper, we present a method that allows the three-dimensional (3D) reconstruction of investigated particle agglomerates using an uncalibrated stereo vision approach that is applied to multiple stereo-pair images. The reconstruction scheme starts with a feature detection and subsequent matching in each pair of stereo images. Based on these correspondences, a robust estimate of the epipolar geometry is determined. A following rectification allows a reduction of the dense correspondence problem to a one-dimensional search along conjugate epipolar lines. So the disparity maps can be obtained using a dense stereo matching algorithm. To remove outliers while preserving edges and individual structures, a disparity refinement is executed using suitable image filtering techniques. The investigated specimen’s qualitative depth’s information can be directly calculated from the determined disparity maps. In a final step the resulting point clouds are registered. State-of-the-art algorithms for 3D reconstruction of SEM micrographs mainly focus on structures whose image pairs contain hardly or even none-occluded areas. The acquisition of multiple stereo-pair images from different perspectives makes it possible to combine the obtained point clouds in order to overcome occurring occlusions. The presented approach thereby enables the 3D illustration of the investigated particle agglomerates.
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