In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. These features enable more comprehensive conditions for safety research and applications such as autonomous vehicle safety and location-based safety analysis. In addition, CitySim facilitates research towards digital twin applications by providing relevant assets like the recording locations'3D base maps and signal timings. Compared to other video-based trajectory datasets, the CitySim Dataset has significantly more critical safety events with higher severity including cut-in, merge, and diverge events. Furthermore, the dataset provides vehicle rotated bounding box information which is demonstrated to improve safety evaluation. CitySim trajectories were generated through a five-step procedure which ensured the trajectory accuracy. It covers a variety of road geometries including freeway basic segments, weaving segments, expressway merge/diverge segments, signalized intersections, stop-controlled intersections, and intersections without sign/signal control. CitySim has vehicle trajectories extracted from 1140-minutes of drone videos recorded at 12 different locations. This paper introduces the CitySim Dataset, which was devised with a core objective of facilitating safety-based research and applications. The development of safety-oriented research ideas and applications requires fine-grained vehicle trajectory data that not only has high accuracy but also captures a substantial number of critical safety events. Although the data set is very limited and very complex, we could achieve a dice score test of \(68.6 \%\) with EFX-UNet for initial segmentation and \(88 \%\) with the Deep UNet in our system.KeywordsDeep LearningHyperspectral imagingCancer detection With EFX-UNet we could reduce the prediction time from 60 s with Deep UNet to only 5 s. The system is tested on real patient data. 16 with our modified Deep UNet to precisely segment the tumor. After that a deeper analysis is carried on for the detected patients using the 8 channels No. The EFX-UNet predicts the probability of the patient having a malignant tumor and yields an initial segmentation. 14 from each cube are used for training and prediction. Based on the results, a new UNet, called Efficient Exception UNet (EFX-UNet), is devised and the two channels No. First, a relevant wavelength analysis has been done in order to detect the most informative channels in the HS cubes to speed up the prediction and reduce the noise. Therefore, an efficient HSI Deep Learning system is highly needed. In such medical applications, the data set is usually very sparse, limited, complex and noisy. An in-vivo data set with 13 hyperspectral (HS) cubes of malignant laryngeal tumors has been collected directly from 13 different patients at Klinikum Braunschweig using a HS camera.
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