@article{12972,
  abstract     = {Embroidery is a long-standing and high-quality approach to making logos and images on textiles. Nowadays, it can also be performed via automated machines that weave threads with high spatial accuracy. A characteristic feature of the appearance of the threads is a high degree of anisotropy. The anisotropic behavior is caused by depositing thin but long strings of thread. As a result, the stitched patterns convey both color and direction. Artists leverage this anisotropic behavior to enhance pure color images with textures, illusions of motion, or depth cues. However, designing colorful embroidery patterns with prescribed directionality is a challenging task, one usually requiring an expert designer. In this work, we propose an interactive algorithm that generates machine-fabricable embroidery patterns from multi-chromatic images equipped with user-specified directionality fields.We cast the problem of finding a stitching pattern into vector theory. To find a suitable stitching pattern, we extract sources and sinks from the divergence field of the vector field extracted from the input and use them to trace streamlines. We further optimize the streamlines to guarantee a smooth and connected stitching pattern. The generated patterns approximate the color distribution constrained by the directionality field. To allow for further artistic control, the trade-off between color match and directionality match can be interactively explored via an intuitive slider. We showcase our approach by fabricating several embroidery paths.},
  author       = {Liu, Zhenyuan and Piovarci, Michael and Hafner, Christian and Charrondiere, Raphael and Bickel, Bernd},
  issn         = {1467-8659},
  journal      = {Computer Graphics Forum},
  keywords     = {embroidery, design, directionality, density, image},
  location     = {Saarbrucken, Germany},
  number       = {2},
  pages        = {397--409},
  publisher    = {Wiley},
  title        = {{Directionality-aware design of embroidery patterns}},
  doi          = {10.1111/cgf.14770 },
  volume       = {42},
  year         = {2023},
}

@article{11615,
  abstract     = {The recently published Kepler mission Data Release 25 (DR25) reported on ∼197 000 targets observed during the mission. Despite this, no wide search for red giants showing solar-like oscillations have been made across all stars observed in Kepler’s long-cadence mode. In this work, we perform this task using custom apertures on the Kepler pixel files and detect oscillations in 21 914 stars, representing the largest sample of solar-like oscillating stars to date. We measure their frequency at maximum power, νmax, down to νmax≃4μHz and obtain log (g) estimates with a typical uncertainty below 0.05 dex, which is superior to typical measurements from spectroscopy. Additionally, the νmax distribution of our detections show good agreement with results from a simulated model of the Milky Way, with a ratio of observed to predicted stars of 0.992 for stars with 10<νmax<270μHz. Among our red giant detections, we find 909 to be dwarf/subgiant stars whose flux signal is polluted by a neighbouring giant as a result of using larger photometric apertures than those used by the NASA Kepler science processing pipeline. We further find that only 293 of the polluting giants are known Kepler targets. The remainder comprises over 600 newly identified oscillating red giants, with many expected to belong to the Galactic halo, serendipitously falling within the Kepler pixel files of targeted stars.},
  author       = {Hon, Marc and Stello, Dennis and García, Rafael A and Mathur, Savita and Sharma, Sanjib and Colman, Isabel L and Bugnet, Lisa Annabelle},
  issn         = {1365-2966},
  journal      = {Monthly Notices of the Royal Astronomical Society},
  keywords     = {Space and Planetary Science, Astronomy and Astrophysics, asteroseismology, methods: data analysis, techniques: image processing, stars: oscillations, stars: statistics},
  number       = {4},
  pages        = {5616--5630},
  publisher    = {Oxford University Press},
  title        = {{A search for red giant solar-like oscillations in all Kepler data}},
  doi          = {10.1093/mnras/stz622},
  volume       = {485},
  year         = {2019},
}

@inproceedings{9943,
  abstract     = {Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.},
  author       = {Sommer, Christoph M and Straehle, Christoph and Köthe, Ullrich and Hamprecht, Fred A.},
  booktitle    = {2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro},
  isbn         = {978-1-4244-4127-3},
  issn         = {1945-8452},
  keywords     = {image segmentation, biomedical imaging, three dimensional displays, neurons, retina, observers, image color analysis},
  location     = {Chicago, Illinois, USA},
  publisher    = {Institute of Electrical and Electronics Engineers},
  title        = {{Ilastik: Interactive learning and segmentation toolkit}},
  doi          = {10.1109/isbi.2011.5872394},
  year         = {2011},
}