Segmentation
Showing 36 of 37 projects
A fast and flexible Python library for image augmentation in computer vision tasks like classification, segmentation, and object detection.
A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
A comprehensive library for image processing in Python with algorithms for segmentation, filtering, morphology, and feature detection.
A procedural Blender pipeline for generating photorealistic training images for computer vision and machine learning.
A comprehensive guide with diagrams and best practices for implementing corporate network segmentation across four security maturity levels.
A free, open-source multi-platform software for 3D visualization and medical image analysis.
A generalist algorithm for cellular segmentation with human-in-the-loop training and superhuman generalization across diverse microscopy images.
An end-to-end Python pipeline for semantic segmentation of aerial and satellite imagery to extract features like buildings and roads.
A desktop application for semi-automatic image annotation using OpenCV's watershed algorithm with manual brush refinement.
A deep learning framework for feature learning directly from point clouds using X-Conv operations, achieving state-of-the-art results in classification and segmentation.
Fast and robust algorithm for segmenting Velodyne LiDAR point clouds into objects for autonomous driving applications.
An open-source library providing chest X-ray datasets, pre-trained models, and tools for medical imaging research and analysis.
A lean and fast C++ library for 3D point cloud data processing with efficient implementations of common operations.
A library of modular computer vision components built on Keras 3, supporting TensorFlow, JAX, and PyTorch backends.
A framework for semantic and instance segmentation of LiDAR point clouds using range images, designed for autonomous driving applications.
A self-contained Japanese morphological analyzer written in pure Go, tokenizing text into words and analyzing parts of speech.
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
A C++ library for fast ground segmentation from LiDAR point clouds using the line-fit algorithm.
Open-source software for 3D medical imaging reconstruction from CT and MRI DICOM files.
A TensorFlow implementation for generating semantically segmented bird's eye view images from multiple vehicle-mounted cameras using a Sim2Real deep learning approach.
Interactive segmentation and tracking tools for microscopy images built on Segment Anything.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A Python implementation for fully automatic extrinsic calibration of 3D LiDAR and cameras using laser reflectance intensity.
ROS & ROS2 implementation of Patchwork++, a fast and robust ground segmentation method for 3D LiDAR point clouds.
FLAME dataset and deep learning models for fire detection in aerial imagery using UAVs, supporting classification and segmentation tasks.
A curated list of open-source software tools for medical imaging research, including segmentation, visualization, and deep learning libraries.
A PyTorch-based segmentation toolbox for electron microscopy connectomics, enabling neural structure analysis in 3D volumes.
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
A curated list of software, tools, pipelines, and plugins for image analysis in biological research.
A scalable cell tracking method for 2D, 3D, and multichannel timelapse recordings, robust under segmentation uncertainty.
A public dataset of field images with segmentation masks and plant type annotations for computer vision in precision agriculture.
Open-source implementation of the winning solution for the 2018 Data Science Bowl Kaggle competition using PyTorch and U-Net.
A deep learning tool for automatic axon and myelin segmentation from microscopy images using convolutional neural networks.
A tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
An automated pipeline for organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy.
A GUI-based tool for training deep neural networks to segment biological images using corrective annotation.
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