Since the ability of quantum computing to speed up certain, specific computations has a solid theoretical underpinning, it is a natural question to ask if problems in computer vision also benefit from it. This doctoral thesis is in particular concerned with the question if the recently emerging optimization method of quantum annealing is useful for solving combinatorial optimization problems that arise in computer vision. The computer vision problem that is mainly investigated here is the problem of shape matching. The task of shape matching is to obtain the correspondences of two objects in a scene that are described with point clouds. Most often, one is concerned with finding the correspondences for a person who is recorded in 3D two times but with a different pose. The reason why this computer vision problem was picked as an interesting candidate for an application of quantum annealing is because it can be formulated as a difficult combinatorial optimization problem with a quadratic objective. This is close to the type of optimization problem that recent quantum annealing devices try to solve. The biggest difference is that in quantum annealing one a priori has unconstrained problems so that one cannot include equality constraints in the optimization. In the first work of this thesis, multiple methods are presented to overcome this difference and for really small shape matching problems experiments with simulated quantum annealing and with quantum annealing on D-Wave quantum computers were presented. In the second work of this thesis an iterative method was developed so that it was even possible to solve shape matching problems with over 500 vertices. Finally, a third publication looked at problems where one needs to find the correspondences between multiple shapes. Besides applications of quantum annealing for shape matching, this thesis also contains work on solving the motion segmentation problem with quantum annealing and work about integrating a quantum annealer in a neural network to solve computer vision problems. The integration of the quantum annealer in a neural network can also be viewed as the task to learn an appropriate formulation of a computer vision problem in a way that is solvable with a current quantum annealer. Finally, we also present work about an iterative method for compensating limited connectivity in quantum annealing devices. This work has no particular computer vision application in mind, but investigates the performance of the different approaches on Max-Cut problems. The thesis is structured in a cumulative way, so that the individual publications are presented separately. On a broader picture, the works are motivated by the following questions: What could be the impact of quantum annealing on computer vision and how could computer vision algorithms that use quantum annealing look like? And from another perspective: How would the field of computer vision be changed if there were massive advances in building quadratic, unconstrained, binary optimization problem solvers with or even without utilizing quantum computing?

Imaging is a leading instrumentation technique for a vast range of applications. Among others, it is used for scene recognition in autonomous driving, robotics, the medical sector and in smartphones. Besides classical color imaging of two-dimensional (2D) planes, precise and accurate distance ranging is one advancing imaging modality, expanding imaging towards three dimensional (3D) depth sensing. Beyond image quality, high density integration of these image sensors into miniaturized systems, even on a single chip, is a major driver of ongoing development. State-of-the-art distance ranging and 3D imaging techniques are Time-of-Flight (ToF) and Triangulation. Both techniques face challenges in terms of small scale integration capabilities. Typical triangulation-based systems require a specific baseline distance in the cm-range, which is not suitable for monolithic chip integration. ToF-systems can be integrated on a single chip, but its principle requires high precision timings in the sub-ns range, advanced beam steering or complex circuity. Here, developments and achievements on photodetectors based on hydrogenated amorphous silicon (a-Si:H) capable for distance ranging and future 3D imaging are presented. The a-Si:H manufacturing technology at temperatures < 300°C is compatible with the standard CMOS. This approach enables the integration of sensors directly on top of a chip in the back end of line with high geometrical fill factors. The distance ranging operation exploits the nonlinear device behavior with respect to the optical excitation scenario. Based on simulations and subsequent experimental validations, two nonlinear mechanisms and their respective physics are systematically investigated, analyzed and developed towards individual distance ranging techniques. The a-Si:H Focus-Induced Photoresponse (FIP) measures nonlinear current response variations originating from an amplitude-modulated monochromatic optical excitation. The distance is acquired by a frequency selective readout of the nonlinear, focus-dependent sensor signal. Distance resolutions down to 540 μm are achieved, allowing for precise ranging at low measurement and integration effort. The a-Si:H Intrinsic Photomixing Detector (IPD), utilizes an envelope mixing process of two amplitude modulated optical excitations. Thereby, the Time-of-Flight information is transferred to a lower frequency domain, enabling a read-out. This mixing takes place intrinsically within the detector and decreases the measurement complexity. Distance measurements are shown at > 70 m with depth resolutions down to 23 mm, which is comparable with commercial systems. Developments on advanced device concepts are studied and discussed. These concepts systematically improve the sensitivity, spectral bandwidth and speed of the detectors.

The workshop report „Hochschuldidaktik 7“  documents and reflects on a number of experiences, concepts, and impulses that have emerged from, and further developed within, the higher education didactics program of the University of Siegen. It highlights the diversity and quality of teaching at Siegen and thereby contributes to enhancing the visibility and appreciation of sustainable teaching innovations. Faculty members are encouraged to share their teaching practices and to serve as a source of inspiration for others—thus fostering a sustainable culture of learning characterized by the visibility and recognition of high-quality university teaching. In this sense, the present publication also constitutes a call to reflect upon, share, and further develop one’s own teaching practice.