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This study aims at evaluating metrics for the measurement of camera vibrations in image sequences considering triangles and bars as test patterns. The focus are objective metrics for video stabilization, which are designed to objectively evaluate whether video stabilization was able to eliminate objectionable visual movement.
The metrics are evaluated for simulated image sequences captured by an artificially moved camera. The sequences vary in different properties such as the sensor noise of the camera, as well as the and temporal frequency of the camera vibrations. We analyze the effect of these properties on the metrics behavior. First results using recorded data of thermal imagers are presented as well. The findings will provide insights into the efficacy of different video stabilization metrics on simulated sequences with varying properties.
In this work, performance assessments for TOD models and YOLO-based models are compared. Known image databases as well as synthetic images with triangles and natural backgrounds are degraded according to a unified device description with blur and image noise. The blur caused by optical diffraction and detector footprint is varied by multiple aperture diameters and detector sizes through the application of modulation transfer functions, while the image noise is varied by multiple noise error levels as Gaussian sensor noise. The TOD models are evaluated for the degraded images with triangles, while the YOLO models are applied to the degraded variants of the image databases. For different degradation parameters, the model precisions of the TOD models are compared to figures of merit of the YOLO models such as the mean average precision (mAP). Statistical uncertainties of the performance ranking for different degradation parameters of cameras and both TOD and YOLO models are investigated.
Near-eye displays–displays positioned in close proximity to the observer’s eye–are a technology continuing to gain significance in industrial and defense applications, e.g. for augmented reality and digital night vision. Fraunhofer IOSB has recently developed a specialized measurement setup for assessing the display capabilities of such devices as part of the optoelectronic imaging chain, with the primary focus on the Modulation Transfer Function (MTF).
The setup consists of an imaging system with a high-resolution CMOS camera and a motorized positioning system. It is intended to run different measurement procedures semi-automatically, performing the desired measurements at specified points on the display.
This paper presents the extended work on near-eye display imaging quality assessment following the initial publication. Using a commercial virtual reality headset as a sample display, we further refined the previously described MTF measurement procedures, with one method being based on bar pattern images and another method using a slanted edge image. Refinements include improvements to the processing of the camera images as well as to the method of extracting contrast measurements. Furthermore, we implemented an additional, line-image-based method for determining the device’s MTF.
The impact of the refinements is examined and the results of the different methods are discussed with the goal to find the most suitable measurement procedures for our setup and to highlight the individual merits of different measurement methods.
Target detection is a crucial task in defense applications such as surveillance, infrared search and track, and missile approach warning systems. Typically, the target image is extended over a few sensor pixels of the imaging system only and the detection is performed by appropriate algorithms.
In order to study the impact of imaging system design parameters and environmental conditions on the detection performance, a simulation tool is developed. Apart from computing detection ranges based on the expected signal to noise ratio the algorithm requires for detection, the tool is also meant for simulating image sequences of engaging targets. Therefore, it provides means to investigate the interplay between system design parameters and algorithms for target detection.
The simulation is based on a rigorous calculation of the target image in the focal plane, with consideration of the optical transfer functions of imaging chain components. Integrating the target image over the active pixel areas yields the additional signal of the detector pixels caused by the target. Based on these values and average background noise the signal to noise ratio (SNR) is obtained as function of the target distance. Image data is generated by overlaying the additional signals over a background image.
We exemplify the application of the simulation tool by studying the effect of various system parameters and environmental conditions on the resulting SNR and detection range. Corresponding simulated image data is presented as well.
In the first part, we outline the experimental setup of our testbed. It allows for mimicking infrared imaging of real scenes in a controlled laboratory environment. We describe the process of dynamic infrared scene generation as well as the physical limitations of our scene projection setup.
A second part discusses ongoing and future applications. This testbed extends our standard lab measurements for thermal imagers by a image based performance analysis method. Scene based methods are necessary to investigate and assess advanced digital signal processing (ADSP) algorithms which are becoming an integral part of thermal imagers. We use this testbed to look into inferences of unknown proprietary ADSP algorithms by choosing suitable test scenes.
Furthermore, we investigate the influence of dazzling on thermal imagers by coupling infrared laser radiation into the projected scene. The studies allow to evaluate the potential and hazards of infrared dazzling and to describe correlated effects. In a future step, we want to transfer our knowledge of VIS/NIR laser protection into the infrared regime.
ECOMOS uses and combines well-accepted existing European tools to build up a strong competitive position. This includes two TA models: the analytical TRM4 model and the image-based TOD model. In addition, it uses the atmosphere model MATISSE.
In this paper, the central idea of ECOMOS is exposed. The overall software structure and the underlying models are shown and elucidated. The status of the project development is given as well as a short discussion of validation tests and an outlook on the future potential of simulation for sensor assessment.
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