Refine
Document Type
- Article (12)
- Doctoral Thesis (2)
- Postprint (1)
Is part of the Bibliography
- yes (15) (remove)
Keywords
- processing (15) (remove)
Institute
- Department Linguistik (7)
- Institut für Physik und Astronomie (3)
- Department Erziehungswissenschaft (2)
- Strukturbereich Kognitionswissenschaften (2)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (1)
- Institut für Biochemie und Biologie (1)
- Institut für Chemie (1)
- Potsdam Research Institute for Multilingualism (PRIM) (1)
Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPU-based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
Previous research has shown that heritage speakers struggle with inflectional morphology. 'Limitations of online resources' for processing a non-dominant language has been claimed as one possible reason for these difficulties. To date, however, there is very little experimental evidence on real-time language processing in heritage speakers. Here we report results from a masked priming experiment with 97 bilingual (Turkish/German) heritage speakers and a control group of 40 non-heritage speakers of Turkish examining regular and irregular forms of the Turkish aorist. We found that, for the regular aorist, heritage speakers use the same morphological decomposition mechanism ('affix stripping') as control speakers, whereas for processing irregularly inflected forms they exhibited more variability (i.e., less homogeneous performance) than the control group. Heritage speakers also demonstrated semantic priming effects. At a more general level, these results indicate that heritage speakers draw on multiple sources of information for recognizing morphologically complex words.
'Complex systems are information processors' is a statement that is frequently made. Here we argue for the distinction between information processing-in the sense of encoding and transmitting a symbolic representation-and the formation of correlations (pattern formation/self-organisation). The study of both uses tools from information theory, but the purpose is very different in each case: explaining the mechanisms and understanding the purpose or function in the first case, versus data analysis and correlation extraction in the latter. We give examples of both and discuss some open questions. The distinction helps focus research efforts on the relevant questions in each case.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.
Shape-Memory Capability of Copolyetheresterurethane Microparticles Prepared via Electrospraying
(2015)
Multifunctional thermo-responsive and degradable microparticles exhibiting a shapememory effect (SME) have attracted widespread interest in biomedicine as switchable delivery vehicles or microactuators. In this work almost spherical solid microparticles with an average diameter of 3.9 +/- 0.9 mm are prepared via electrospraying of a copolyetheresterurethane named PDC, which is composed of crystallizable oligo(p-dioxanone) (OPDO) hard and oligo(e-caprolactone) (OCL) switching segments. The PDC microparticles are programmed via compression at different pressures and their shapememory capability is explored by off-line and online heating experiments. When a low programming pressure of 0.2 MPa is applied a pronounced thermally-induced shape-memory effect is achieved with a shape recovery ratio about 80%, while a high programming pressure of 100 MPa resulted in a weak shape-memory performance. Finally, it is demonstrated that an array of PDC microparticles deposited on a polypropylene (PP) substrate can be successfully programmed into a smart temporary film, which disintegrates upon heating to 60 degrees C.