MXE cross compileation: image not loading in windows #windows #qt #imageprocessing #crosscompilation #widgets

https://askubuntu.com/q/1550793/612

Lightzone failure on 24.04 Noble #2404 #ppa #imageprocessing

https://askubuntu.com/q/1550325/612

From Curtis Rueden @ctrue , lead Fiji developer:

"Fiji’s migration to Java 21 took a huge step forward today: you can now download Fiji-Latest from https://fiji.sc that bundles Java 21."

For users, please download and test, and let us know what doesn't work – there's been a lot of testing already plus the compile-time guarantees of Java, but alas, nothing beats using it.

For developers, please test your plugins and libraries.

https://forum.image.sc/t/fiji-latest-java-21-bundles-ready-for-community-testing/112417

Keeping on track with Krita (alpha6 build from master source) and Qt6 builds together with upstream developers, with PyQt6 enabled I can now also use the python scripts in Krita on Haiku. (not that I would know how to, as much as I don't know how to code)

Overcoming barriers in the use of artificial intelligence in point of care ultrasound https://www.byteseu.com/936518/ #AI #ArtificialIntelligence #Biomedicine #Biotechnology #General #ImageProcessing #MachineLearning #Medicine/PublicHealth

A critical assessment of artificial intelligence in magnetic resonance imaging of cancer https://www.byteseu.com/904932/ #AI #ArtificialIntelligence #BiologicalMicroscopy #BiomedicalEngineering #CancerImaging #ComputerAppl.InLifeSciences #ComputerImaging #ImageProcessing #Imaging/Radiology #PatternRecognitionAndGraphics #SpectroscopyAndMicroscopy #Vision

إطلاق الإصدار v3.5.3 من G'MIC!

يسرنا الإعلان عن إصدار جديد من G'MIC، صندوق أدوات الفلاتر المفضل لديك!

ما الجديد؟

إضافة فلاتر جديدة.

تحسينات في الأداء.

إصلاح الأخطاء.

تطوير فخور: تم تطويره في @GreycLab بواسطة باحثي @CNRS المتحمسين لمعالجة الصور!

التحميل: https://gmic.eu/download.html
سجل التغييرات: https://discuss.pixls.us/t/on-the-road-to-3-6

Brighten an image's saturated pixels with Python and PIL
https://rodstephensbooks.com/pil_brighten_saturated_pixels.html
#Python #ImageProcessing

Interactively overlay parts of one image on another with Python and PIL
https://rodstephensbooks.com/pil_overlay_interactive.html
#Python #ImageProcessing

Use a mask to overlay one image on another with Python and PIL
https://rodstephensbooks.com/pil_overlay_with_mask.html
#Python #ImageProcessing

Perform binary contrast enhancement with Python and PIL
https://rodstephensbooks.com/pil_binary_contrast_enhancement.html
#Python #ImageProcessing

Map a trapezoidal image onto an isosceles trapezoid in Python
https://rodstephensbooks.com/rectify_trapezoid.html
#Python #ImageProcessing

Map a trapezoidal image onto a rectangle in Python
https://rodstephensbooks.com/rectify.html
#Python #ImageProcessing

Building a simple medical image segmentation tool in #JuliaLang.

So far the method only creates raw contours. Next I need to add contour cutting, merging, spline based smoothing, growing/shrinking, manual drawing, etc. and before you know it we have a high performance and #opensource tool for medical image segmentation!

ITK 5.4.0 Released!

The Insight Toolkit (ITK) 5.4.0 brings major documentation improvements and modern C++ enhancements:

- Migration to Markdown-based system on ReadTheDocs
- Unified search across all documentation sources
- Python 3.11+ support with Stable ABI wheels
- C++17 required with modern features like structured bindings
- Enhanced DICOM and NRRD metadata support

Full release notes: https://docs.itk.org/en/latest/releases/5.4.0.html

I came across an interesting but absolutely pointless thing.

It sorts pixels by color from a loaded image.

You can try it here: https://solst-ice.github.io/pxl-srt/

#ConvolutionalNeuralNetworks (#CNNs in short) are immensely useful for many #imageProcessing tasks and much more...

Yet you sometimes encounter some bits of code with little explanation. Have you ever wondered about the origins of the values for image normalization in #imagenet ?

• Mean: [0.485, 0.456, 0.406] (for R, G and B channels respectively)
• Std: [0.229, 0.224, 0.225]

Strangest to me is the need for a three-digits precision. Here, after finding the origin of these numbers for MNIST and ImageNet, I am testing if that precision is really important : guess what, it is not (so much) !

if interested in more details, check-out https://laurentperrinet.github.io/sciblog/posts/2024-12-09-normalizing-images-in-convolutional-neural-networks.html

DIPlib 3.5.2 released!

We've added the graph-cut algorithm, and finally have documentation in Python.

https://www.crisluengo.net/archives/1147/