Star102 Code Issues Pull requests 🖼️ A tool for efficient processing of spectral images with Python. pythonmachine-learningspectral-analysisspectral-datahyperspectral-imagesspectral-imaging UpdatedJan 6, 2025 Python Band-Adaptive Spectral-Spatial Feature Learning Deep Neural Network for Hyperspectral Im...
PlantSegis a tool for cell instance aware segmentation in densely packed 3D volumetric images. The pipeline uses a two stages segmentation strategy (Neural Network + Segmentation). The pipeline is tuned for plant cell tissue acquired with confocal and light sheet microscopy. Pre-trained models are ...
A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labele...
Here, you’ll also see a number of example images and best practices for the tool. Nikita’s description of this program is as follows: “StarNet is a neural network that can remove stars from images in one simple step leaving only the background. More technically, it is a convolutional...
Twin networks are particularly useful in cases where there are large numbers of classes with small numbers of observations of each. In such cases, there is not enough data to train a deep convolutional neural network to classify images into these classes. Instead, the twin network can determine...
Milky Way. It’s the second direct image of a black hole. Like the first such image — of the black hole at the centre of galaxy M87, released in 2019 — astronomers created the picture using radio-wave observations taken by the Event Horizon Telescope, a global network of radio ...
With blocked images, as with nonblocked images, you can take advantage of automated processing to help with the labeling process. For example, with medical images, you can do automatic tumor detection using blocked image automation. You can train an object detector deep learning network and use ...
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Below the Image Detection Settings is also an updated preview of the binary image that will be obtained with the current settings. The goal is to produce an image in which the expected network appears in white and is connected. The background of the image should appear as black pixels. ...
context, and do so during natural scene stimulation. We designed a non-linear model to explain and predict these changes, and mapped this model to specific circuits in the retinal network. Finally, we demonstrated that this strong context dependence is compatible with a robust computation of a ...