Showing 4 open source projects for "artificial intelligence based games source code"

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  • 1
    CometAnalyser

    CometAnalyser

    CometAnalyser, for quantitative comet assay analysis.

    Description: Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. To obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. CometAnalyser is an open-source deep-learning tool designed for the analysis of both fluorescent and silver-stained wide-field microscopy images. Once the comets are segmented and classified, several intensity/morphological features are automatically exported as a...
    Downloads: 14 This Week
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  • 2
    Spheroid_segmentation

    Spheroid_segmentation

    Deep learning networks for spheroid segmentation

    To accelerate the analysis of tumors' spheroids, different deep learning networks were trained to automatize the segmentation process. The code provides the trained networks based on Vgg16, Vgg19, ResNet18, and ResNet50 ready to be used for segmentation purposes. It also provides Matlab functions ready to be used to train new networks, segment new images, and measure the quality of the training using different quantitative parameters.
    Downloads: 0 This Week
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  • 3
    Robust Tube MPC

    Robust Tube MPC

    Example implementation for robust model predictive control using tube

    robust-tube-mpc is a MATLAB implementation of robust tube-based Model Predictive Control (MPC). The framework provides tools to design and simulate controllers that maintain stability and constraint satisfaction in the presence of bounded disturbances. Tube-based MPC achieves robustness by combining a nominal trajectory planner with an error feedback controller that keeps the actual system state within a "tube" around the nominal trajectory. This repository includes example scripts and...
    Downloads: 4 This Week
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  • 4
    Detect and Track

    Detect and Track

    Code release for "Detect to Track and Track to Detect", ICCV 2017

    Detect-Track is the official implementation of the ICCV 2017 paper Detect to Track and Track to Detect by Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman. The framework unifies object detection and tracking into a single pipeline, allowing detection to support tracking and tracking to enhance detection performance. Built upon a modified version of R-FCN, the code provides implementations using backbone networks such as ResNet-50, ResNet-101, ResNeXt-101, and Inception-v4, with...
    Downloads: 0 This Week
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