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SUMMARY:image analysis and visualization in Python with scikit-image\, nap
 ari\, and friends - Juan Nunez-Iglesias\, Lars Grüter\, Kira Evans
DTSTART;TZID=America/Chicago:20230710T080000
DTEND;TZID=America/Chicago:20230710T120000
DTSTAMP:20261007T034045Z
UID:pretalx-2023-NEUUKG@cfp.scipy.org
DESCRIPTION:Between telescopes and satellite cameras and MRI machines and 
 microscopes\, scientists are producing more images than they can realistic
 ally look at. They need specialized viewers for multi-dimensional images\,
  and automated tools to help process those images into knowledge. In this 
 tutorial\, we will cover the fundamentals of algorithmic image analysis\, 
 starting with how to think of images as NumPy arrays\, moving on to basic 
 image filtering\, and finishing with a complete workflow: segmenting a 3D 
 image into regions and making measurements on those regions. At every step
 \, we will visualize and understand our work using matplotlib and napari.
LOCATION:Classroom 202
URL:https://cfp.scipy.org/2023/talk/NEUUKG/
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SUMMARY:View\, annotate\, and analyze multi-dimensional images in Python w
 ith napari - Juan Nunez-Iglesias
DTSTART;TZID=America/Chicago:20230713T163000
DTEND;TZID=America/Chicago:20230713T170000
DTSTAMP:20261007T034045Z
UID:pretalx-2023-X8KZ3E@cfp.scipy.org
DESCRIPTION:napari is an n-dimensional image viewer for Python. If you’v
 e ever tried `plt.imshow(arr)` and made Matplotlib unhappy because `arr` h
 as more than two dimensions\, then napari might be for you! napari will gl
 adly *display higher-dimensional arrays* by providing sliders to explore a
 dditional dimensions. But napari can also: *overlay* derived data\, such a
 s points\, segmentations\, polygons\, surfaces\, and more\; and *annotate*
  and *edit* these data\, using standard data structures like NumPy or Zarr
  arrays\, allowing you to *seamlessly weave* exploration\, computation\, a
 nd annotation in image analysis.
LOCATION:Amphitheater 204
URL:https://cfp.scipy.org/2023/talk/X8KZ3E/
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