Niels-Ole Rohweder

In this paper, we evaluate the effect of lossy compression on luminance-based area measurements in images. On a set of 1,800 images, we vary three parameters: compression quality, edge width, and edge gradient, of the luminance edge bounding the area. As a result, when using the JPEG codec, the root-mean-square error after compression approximately doubles when decreasing the edge width from 16 to 4 pixel while keeping the gradient fixed. Conversely, for a fix width, the root-mean-square error doubles when halving the gradient. We also compared JPEG with the H.264 codec using default settings. H.264, based on inter-frame compression, requires four to six times the bitrate to achieve the results of JPEG. We conclude that for area measurements like these, among lossy codecs, intra-frame compression like JPEG is the best choice. Whether the influence of the compression is relevant depends on individual requirements, and more on the edge gradient than the edge width. Generally, we found that for steep gradients and reasonable JPEG compressions of Q = 85 or higher, our results were acceptable.

Zusammenfassung: Es kann aus verschiedenen Gründen nötig sein, günstige Consumer-Kameras für Labormessungen einzusetzen. Diese haben jedoch häufig nur einen Videostream mit verlustbehafteter Komprimierung. Wir zeigen in diesem Artikel, am Beispiel des Motion-JPEG (MJPEG)-Codecs, dass dies für helligkeitsbasierte Flächenmessungen keine Einschränkungen bedeuten muss. Zentrale Rolle spielt hier der Hell/Dunkel-Übergang, welcher die Fläche eingrenzt. Der Effekt von JPEG auf solche Kanten wird jedoch in der Literatur nur für Kantenbreiten von wenigen Pixeln untersucht. Wir implementieren den wichtigsten Schritt der JPEG-Komprimierung in Matlab, und vergleichen zwei Messreihen zu Flächenmessungen aus jeweils 1.800 Bildern einer Pupille und eines Metalloches. Dabei werden der Qualitätsfaktor des JPEG-Codecs und die Breite des Hell/Dunkel-Überganges variiert. Als Ergebnis liegt die mittlere quadratische Abweichung der Flächenmaße nach Komprimierung für alle untersuchten Breiten (zwischen 4 und 22,5 Pixeln) und alle ausgewerten Qualitätsfaktoren (zwischen 30 und 90) unterhalb der Standardabweichung der Flächenmessung des Metalloches. Im Detail ergibt sich schließlich eine weitere Abhängigkeit von der Steilheit des Hell/Dunkel-Überganges.

Article High precision pupillometry was published on September 1, 2023 in the journal tm - Technisches Messen (volume 90, issue s1).

Recent research indicates that a direct correlation exists between brain activity and oscillations of the pupil. A publication by Park and Whang shows measurements of excitations in the frequency range below 1 Hz. A similar correlation for frequencies between 1 Hz and 40 Hz has not yet been clarified. In order to evaluate small oscillations, a pupillometer with a spatial resolution of 1 µm is required, exceeding the specifications of existing systems. In this paper, we present a setup able to measure with such a resolution. We consider noise sources, and identify the quantisation noise due to finite pixel sizes as the fundamental noise source. We present a model to describe the quantisation noise, and show that our algorithm to measure the pupil diameter achieves a sub-pixel resolution of about half a pixel of the image or 12 µm. We further consider the processing gains from transforming the diameter time series into frequency space, and subsequently show that we can achieve a sub-micron resolution when measuring pupil oscillations, surpassing established pupillometry systems. This setup could allow for the development of a functional optical, fully-remote electroencephalograph (EEG). Such a device could be a valuable sensor in many areas of AI-based human-machine-interaction.

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.