ohne Studienabschlussarbeiten

Fast and non-contacting 3D shape measurements of objects for quality assurance, human machine interaction, or robot handling, e.g., in the industrial sector, have become well established. Recently, we have successfully combined thermography and triangulation to tackle the challenge of measuring the 3D shape of uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent. Therefore, we have developed the principle of thermal 3D measurements, a two-step process consisting of (1) the projection and absorption of projection patterns in the thermal infrared and (2) the stereo recording of heat patterns re-emitted by the object surface. We match image points by evaluating the temporal normalized cross correlation between pixels in both camera image stacks. In order to measure dynamic scenes, the previously achieved measurement times of a few seconds must be reduced by at least one order of magnitude to the range < 0.1 s. For this purpose, we apply established single-shot methods from the visible spectral range to our thermal 3D approach. Instead of temporal sequences of multi-fringe patterns or scanning single fringes, we now project statistical point patterns and record only one thermal stereo image pair. In this paper, we theoretically investigate our approach by using a simulation model for thermal point pattern generation on static measurement objects. We analyze the temporal and spatial behavior of the heat patterns taking the material parameters into account. Finally, we show a first thermal single-shot 3D measurement.

In the context of collaborative robotics, handing over hand-held objects to a robot is a safety-critical task. Therefore, a robust distinction between human hands and presented objects in image data is essential to avoid contact with robotic grippers. To be able to develop machine learning methods for solving this problem, we created the OHO (Object Hand-Over) dataset of tools and other everyday objects being held by human hands. Our dataset consists of color, depth, and thermal images with the addition of pose and shape information about the objects in a real-world scenario. Although the focus of this paper is on instance segmentation, our dataset also enables training for different tasks such as 3D pose estimation or shape estimation of objects. For the instance segmentation task, we present a pipeline for automated label generation in point clouds, as well as image data. Through baseline experiments, we show that these labels are suitable for training an instance segmentation to distinguish hands from objects on a per-pixel basis. Moreover, we present qualitative results for applying our trained model in a real-world application.

We study the task of object categorization in an industrial setting. Typically, a machine classifies objects according to an internal, inferred model, and calls to a human worker if it is uncertain. However, the human worker may be also uncertain. We elaborate on the challenges and solutions to assess the certainty of the human without disturbing the industrial process, and to assess label reliability and human certainty in conventional object classification and crowdworking. Albeit there are methods for measuring stress, insights on the correlation of stress and uncertainty and uncertainty indicators during labeling by humans, these advances are yet to be combined to solve the aforementioned uncertainty challenge. We propose a solution as a sequence of tasks, starting with a experiment that measures human certainty in a task of controlled difficulty, whereupon we can associate certainty with correctness and levels of vital signals.

The effects of climate change, such as drought and pest infestation, will pose new challenges for forest management in the coming years to ensure the preservation of biodiversity and vegetation balance. A combination of various sensor technologies enables early detection of changes and initiation of necessary mitigation steps. Here, hyperspectral cameras provide direct measurement of the health status on the plants themselves. The achievable spatial and spectral resolutions have been steadily increasing due to the use of drones instead of airplanes and satellites. Nevertheless, only canopy measurement is possible in this case. The measurement below the tree canopy can grant new insights and increase the resolution up to the level of the leaf. The aim of this work is to define the basic requirements for a spectral system suitable for this purpose. For these high-resolution spectral images of typical plants of the mid-mountain range during desiccation were acquired. On the basis of these, various vegetation indices were calculated and the influence of filter properties such as the half-width were simulated. During this investigation, a clear reaction to desiccation was observed in all samples after a brief period of time. Different vegetation indices show a comparable behavior despite the application of different wavelengths.

When using a molding machine to produce plastic samples, unwanted residuals can occur. Within this study two image processing methods for the detection of residuals at plastic samples are evaluated. The aim of the two suggested methods is to detect the position of the residuals at the plastic sample reliable and to transform the image-based information into laser machine coordinates. By using the transferred coordinates, the laser machine can remove the detected residuals by laser cutting accurately without damaging the sample. The measurement setup for both methods is identical, the difference is in the processing of the captured raw image. The first method compares the raw image with the image masking template to determine the residual. The second method processes the raw image directly by comparing the light intensity transmitted through the sample to distinguish the residual from the main sample. Once the residuals can be detected, binary shifting are then performed to locate the cut lines for the residuals. The lines obtained from the image in pixel scale must then be accurately converted into millimeter-scale so that the laser machine can use them. By comparing the two methods mentioned above, the method that uses template images has more accurate and detailed results, leaving no small residuals on the sample. Meanwhile, in the method that compares the intensity of the transmitted light through the sample, there were undetectable residuals that did not produce the desired straight line. However, using the image template-matching method has some drawbacks, such as requiring each measurement to be in the same position. And thus, a more detailed design process is needed to stabilize the measurement process. In this study, a design has been made in terms of hardware as well as software with a GUI that can set several important parameters for measurement. From the results of this study, we obtained a system that can cut the residuals on the sample without damaging the sample.