Scanning from heating: 3D shape estimation of transparent objects from local surface heating

Today, with quality becoming increasingly important, each product requires three-dimensional in-line quality control. On the other hand, the 3D reconstruction of transparent objects is a very difficult problem in computer vision due to transparency and specularity of the surface. This paper proposes a new method, called Scanning From Heating (SFH), to determine the surface shape of transparent objects using laser surface heating and thermal imaging. Furthermore, the application to transparent glass is discussed and results on different surface shapes are presented

A 3D scanner for transparent glass

Many practical tasks in industry, such as automatic inspection or robot vision, often require the scanning of three-dimensional shapes by use of non-contact techniques. However, few methods have been proposed to measure three-dimensional shapes of transparent objects because of the difficulty of dealing with transparency and specularity of the surface. This paper presents a 3D scanner for transparent glass objects based on Scanning From Heating (SFH), a new method that makes use of local surface heating and thermal imaging

Computational Depth-resolved Imaging and Metrology

In this thesis, the main research challenge boils down to extracting 3D spatial information of an object from 2D measurements using light. Our goal is to achieve depth-resolved tomographic imaging of transparent or semi-transparent 3D objects, and to perform topography characterization of rough surfaces. The essential tool we used is computational imaging, where depending on the experimental scheme, often indirect measurements are taken, and tailored algorithms are employed to perform image reconstructions. The computational imaging approach enables us to relax the hardware requirement of an imaging system, which is essential when using light in the EUV and x-ray regimes, where high-quality optics are not readily available. In this thesis, visible and infrared light sources are used, where computational imaging also offers several advantages. First of all, it often leads to a simple, flexible imaging system with low cost. In the case of a lensless configuration, where no lenses are involved in the final image-forming stage between the object and the detector, aberration-free image reconstructions can be obtained. More importantly, computational imaging provides quantitative reconstructions of scalar electric fields, enabling phase imaging, numerical refocus, as well as 3D imaging

Documentation of landslides and inaccessible parts of a mine using an unmanned uav system and methods of digital terrestrial photogrammetry

Quite a big boom has recently been experienced in the technology of unmanned aerial vehicles (UAV). In conjunction with dense matching system, it gives one a powerful tool for the creation of digital terrain models and orthophotomaps. This system was used for the documentation of landslides and inaccessible parts of the Nástup Tušimice mine in the North Bohemian Brown Coal Basin (Czech Republic). The images were taken by the GATEWING X100 unmanned system that automatically executed photo flights an area of interest. For detailed documentation of selected parts of the mine, we used the method of digital terrestrial photogrammetry. The main objective was to find a suitable measurement technology for operational targeting of landslides and inaccessible parts of the mine, in order to prepare the basics for remediation work

Deep learning in computational microscopy

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio

Optical coherence tomography for the non-invasive investigation of the microstructure of ancient Egyptian faience

Optical Coherence Tomography (OCT) is a non-invasive subsurface 3D imaging technique based on the Michelson interferometer. The non-invasive nature of OCT and its speed of acquisition makes it possible to image large volumes of intact objects to yield a complete overview of the microstructure. The production methods for ancient Egyptian faience were first investigated using scanning electron microscopy (SEM) imaging of the microstructure in polished sections and microprobe analysis of the composition of the glass phases. These studies were based on original Egyptian faience objects and laboratory reproductions of faience beads made using three different production methods. The microstructure of the same laboratory samples and the Egyptian faience objects from the British Museum Research Laboratory Collection are re-examined using OCT