rOpenSci | Image & Audio Processing

Image & Audio Processing

Use Image & Audio Data
Showing 10 of 12

Advanced Graphics and Image-Processing in R

Jeroen Ooms
Description

Bindings to ImageMagick: the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. The latest version of the package includes a native graphics device for creating in-memory graphics or drawing onto images using pixel coordinates.

Scientific use cases
  1. Stachelek, J., Ford, C., Kincaid, D., King, K., Miller, H., & Nagelkirk, R. (2017). The National Eutrophication Survey: lake characteristics and historical nutrient concentrations. Earth System Science Data Discussions, 1–11. https://doi.org/10.5194/essd-2017-52
  2. Mendez, P. K., Lee, S., & Venter, C. E. (2018). Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys, 795, 49–65. https://doi.org/10.3897/zookeys.795.28416
  3. Weishäupl, D., Schneider, J., Peixoto Pinheiro, B., Ruess, C., Dold, S. M., von Zweydorf, F., … Schmidt, T. (2018). Physiological and pathophysiological characteristics of ataxin-3 isoforms. Journal of Biological Chemistry, jbc.RA118.005801. https://doi.org/10.1074/jbc.ra118.005801
  4. Evans, L. K., & Nishioka, J. (2018). Accumulation processes of trace metals into Arctic sea ice: distribution of Fe, Mn and Cd associated with ice structure. Marine Chemistry. https://doi.org/10.1016/j.marchem.2018.11.011
  5. Maia, R., Gruson, H., Endler, J. A., & White, T. E. (2018). pavo 2: new tools for the spectral and spatial analysis of colour in R. https://doi.org/10.1101/427658
  6. Salazar, P. C., Navarro-Cerrillo, R. M., Cruz, G., Grados, N., & Villar, R. (2019). Variability in growth and biomass allocation and the phenotypic plasticity of seven Prosopis pallida populations in response to water availability. Trees. https://doi.org/10.1007/s00468-019-01868-9
  7. Logemann, A., Schafberg, M., & Brockmeyer, B. (2019). Using the HPTLC-bioluminescence bacteria assay for the determination of acute toxicities in marine sediments and its eligibility as a monitoring assessment tool. Chemosphere. https://doi.org/10.1016/j.chemosphere.2019.05.246
  8. Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLOS Biology, 17(12), e3000494. https://doi.org/10.1371/journal.pbio.3000494
  9. Mowinckel, A. M., & Vidal-Piñeiro, D. (2019). Visualisation of Brain Statistics with R-packages ggseg and ggseg3d. arXiv preprint arXiv:1912.08200 https://arxiv.org/abs/1912.08200
  10. Schwalb‐Willmann, J., Remelgado, R., Safi, K., & Wegmann, M. (2020). moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13374
  11. Michaels, I. H., Pirani, S. J., & Carrascal, A. (2020). Visualizing 50 Years of Cancer Mortality Rates Across the US at Multiple Geographic Levels Using a Synchronized Map and Graph Animation. Preventing Chronic Disease, 17. https://doi.org/10.5888/pcd17.190286
  12. Feldmann, M. J., Hardigan, M. A., Famula, R. A., López, C. M., Tabb, A., Cole, G. S., & Knapp, S. J. (2020). Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry. GigaScience, 9(5). https://doi.org/10.1093/gigascience/giaa030
  13. Biber‐Freudenberger, L., Ergeneman, C., Förster, J. J., Dietz, T., & Börner, J. (2020). Bioeconomy futures: Expectation patterns of scientists and practitioners on the sustainability of bio‐based transformation. Sustainable Development. https://doi.org/10.1002/sd.2072
  14. Berkel, C., & Cacan, E. (2020). GAB2 and GAB3 are expressed in a tumor stage-, grade- and histotype-dependent manner and are associated with shorter progression-free survival in ovarian cancer. Journal of Cell Communication and Signaling. https://doi.org/10.1007/s12079-020-00582-3
  15. Sodhi, K., Wang, X., Chaudhry, M. A., Lakhani, H. V., Zehra, M., Pratt, R., … Shapiro, J. I. (2020). Central Role for Adipocyte Na,K-ATPase Oxidant Amplification Loop in the Pathogenesis of Experimental Uremic Cardiomyopathy. Journal of the American Society of Nephrology, 31(8), 1746–1760. https://doi.org/10.1681/asn.2019101070
  16. Pregla, D., Lissón, P., Vasishth, S., Burchert, F., & Stadie, N. (2020, September 18). Variability in sentence comprehension in aphasia in German. https://doi.org/10.31234/osf.io/7hfpx
  17. Ostrop, J., Zwiggelaar, R., Pedersen, M. T., Gerbe, F., Bösl, K., Lindholm, H. T., … Oudhoff, M. J. (2020). A semi-automated organoid screening method demonstrates epigenetic control of intestinal epithelial differentiation. https://doi.org/10.1101/2020.07.23.217414
  18. Ingenloff, K., & Peterson, A. T. (2020). Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13523
  19. Liang, X., Hu, Y., Yan, C., & Xu, K. (2020). i2d: an R package for simulating data from images and the implications in biomedical research. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa991
  20. Wang, J., Wang, X., Gao, Y., Lin, Z., Chen, J., Gigantelli, J., … Pierre, S. V. (2020). Stress Signal Regulation by Na/K-ATPase As a New Approach to Promote Physiological Revascularization in a Mouse Model of Ischemic Retinopathy. Investigative Opthalmology & Visual Science, 61(14), 9. https://doi.org/10.1167/iovs.61.14.9
  21. Loser, D., Schaefer, J., Danker, T., Möller, C., Brüll, M., Suciu, I., … Kraushaar, U. (2020). Human neuronal signaling and communication assays to assess functional neurotoxicity. Archives of Toxicology, 95(1), 229–252. https://doi.org/10.1007/s00204-020-02956-3
  22. Ball, J. (2020). Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment Analysis. arXiv preprint arXiv:2012.03170. https://arxiv.org/pdf/2012.03170
  23. Hillary, R. F., & Marioni, R. E. (2020). MethylDetectR: a software for methylation-based health profiling. Wellcome Open Research, 5, 283. https://doi.org/10.12688/wellcomeopenres.16458.1
  24. Sellés Vidal, L., Ayala, R., Stan, G.-B., & Ledesma-Amaro, R. (2021). rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families. PLOS ONE, 16(1), e0245280. doi:10.1371/journal.pone.0245280
  25. Mann, D. C., Fitch, W. T., Tu, H.-W., & Hoeschele, M. (2021). Universal principles underlying segmental structures in parrot song and human speech. Scientific Reports, 11(1). doi:10.1038/s41598-020-80340-y
View Documentation

Bindings to OpenCV Computer Vision Library

Jeroen Ooms
Description

Experimenting with computer vision and machine learning in R. This package exposes some of the available OpenCV https://opencv.org/ algorithms, such as edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

View Documentation

Working with Audio and Video in R

Jeroen Ooms
Description

Bindings to FFmpeg http://www.ffmpeg.org/ AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. Also offers high performance tools for reading raw audio, creating spectrograms, and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities.

View Documentation

Open Source OCR Engine

Jeroen Ooms
Description

Bindings to Tesseract https://opensource.google/projects/tesseract: a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.

Scientific use cases
  1. Stachelek, J., Ford, C., Kincaid, D., King, K., Miller, H., & Nagelkirk, R. (2017). The National Eutrophication Survey: lake characteristics and historical nutrient concentrations. Earth System Science Data Discussions, 1–11. https://doi.org/10.5194/essd-2017-52
  2. Bayer, D., & Michael, S. (2019). Exploring the Daschle Collection using Text Mining. arXiv preprint arXiv:1904.12623 https://arxiv.org/pdf/1904.12623
  3. Tennant, W. S. D., Tildesley, M. J., Spencer, S. E. F., & Keeling, M. J. (2020). Climate drivers of plague epidemiology in British India, 1898–1949. Proceedings of the Royal Society B: Biological Sciences, 287(1928), 20200538. https://doi.org/10.1098/rspb.2020.0538
  4. Candarli, D. (2020). A longitudinal study of multi-word constructions in L2 academic writing: the effects of frequency and dispersion. Reading and Writing. https://doi.org/10.1007/s11145-020-10108-3
  5. Hines, R. E., Grandage, A. J., & Willoughby, K. G. (2020). Staying Afloat: Planning and Managing Climate Change and Sea Level Rise Risk in Florida’s Coastal Counties. Urban Affairs Review, 107808742098052. https://doi.org/10.1177/1078087420980526
View Documentation

A Shiny Application for Automatic Measurements of Tree-Ring Widths on Digital Images

Jingning Shi
Description

Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. Users can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. The package provides a Shiny-based application, allowing R beginners to easily analyze tree ring images and export ring-width series in standard file formats.

Scientific use cases
  1. Guiterman, C. H., Lynch, A. M., & Axelson, J. N. (2020). dfoliatR: An R package for detection and analysis of insect defoliation signals in tree rings. Dendrochronologia, 63, 125750. https://doi.org/10.1016/j.dendro.2020.125750
  2. Makela, K., Ophelders, T., Quigley, M., Munch, E., Chitwood, D., & Dowtin, A. (2020). Automatic Tree Ring Detection using Jacobi Sets. arXiv preprint arXiv:2010.08691 https://arxiv.org/abs/2010.08691.
View Documentation
ijtiff
CRAN

Comprehensive TIFF I/O with Full Support for ImageJ TIFF Files

Rory Nolan
Description

General purpose TIFF file I/O for R users. Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from ImageJ and write TIFF files than can be correctly read by ImageJ https://imagej.nih.gov/ij/. Also supports text image I/O.

Scientific use cases
  1. Nolan, R., & Padilla-Parra, S. (2018). ijtiff: An R package providing TIFF I/O for ImageJ users. Journal of Open Source Software, 3(23), 633. https://doi.org/10.21105/joss.00633
  2. Hoffman, M. M., Zylla, J. S., Bhattacharya, S., Calar, K., Hartman, T. W., Bhardwaj, R. D., … Messerli, S. M. (2020). Analysis of Dual Class I Histone Deacetylase and Lysine Demethylase Inhibitor Domatinostat (4SC-202) on Growth and Cellular and Genomic Landscape of Atypical Teratoid/Rhabdoid. Cancers, 12(3), 756. https://doi.org/10.3390/cancers12030756
  3. Germani, E., Lelouard, H., & Fallet, M. (2020). SAPHIR: a Shiny application to analyze tissue section images. F1000Research, 9, 1276. https://doi.org/10.12688/f1000research.27062.1
View Documentation
pixelclasser
Peer-reviewed

Classifies Image Pixels by Colour

Carlos Real
Description

Contains functions to classify the pixels of an image file (jpeg or tiff) by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.

View Documentation