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OpenCV is a huge image and video processing library designed to work with many languages such as python, C/C++, Java, and more. It is the foundation for many of the applications you know that deal ...
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. [2] Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel [3]).
ilastik [1] is a user-friendly free open source software for image classification and segmentation. No previous experience in image processing is required to run the software. Since 2018 ilastik is further developed and maintained by Anna Kreshuk's group at European Molecular Biology Laboratory.
Computer Vision Annotation Tool (CVAT) is an open source, web-based image and video annotation tool used for labeling data for computer vision algorithms. Originally developed by Intel, CVAT is designed for use by a professional data annotation team, with a user interface optimized for computer vision annotation tasks.
Built on top of OpenCV, a widely used computer vision library, Albumentations provides high-performance implementations of various image processing functions. It also offers a rich set of image transformation functions and a simple API for combining them, allowing users to create custom augmentation pipelines tailored to their specific needs. [3]
scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation , geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection , and more. [ 3 ]
OpenCV's Cascade Classifiers support LBPs as of version 2. VLFeat , an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation . LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem.
Intel IPP is divided into four major processing groups: signal processing (with linear array or vector data), image processing (with 2D arrays for typical color spaces), data compression, and cryptography. [6] Half the entry points are of the matrix type, a third are of the signal type, and the remainder are of the image and cryptography types.