Search results
Results from the WOW.Com Content Network
Manual image annotation is the process of manually defining regions in an image and creating a textual description of those regions. Such annotations can for instance be used to train machine learning algorithms for computer vision applications. This is a list of computer software which can be used for manual annotation of images.
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.
Version 3.0, supporting volumetric analysis of 3D image stacks and optional deep learning modules, was released in October 2017. [16] CellProfiler 4.0 was released in September 2020 and focused on speed, usability, and utility improvements with most notable example of migration to Python 3.
Images, text Classification, object detection 2010 [34] [35] M. Everingham et al. CIFAR-10 Dataset Many small, low-resolution, images of 10 classes of objects. Classes labelled, training set splits created. 60,000 Images Classification 2009 [18] [36] A. Krizhevsky et al. CIFAR-100 Dataset Like CIFAR-10, above, but 100 classes of objects are given.
Labelled images, segmented images, 5544 Images Classification, detection 2017 [317] Giselsson et al. Fruits-360 Database with images of 131 fruits and vegetables. 100x100 pixels, white background. 90483 Images (jpg) Classification 2017–2024 [318] Mihai Oltean Weed-ID.App Database with 1,025 species, 13,500+ images, and 120,000+ characteristics
The LightGBM algorithm utilizes two novel techniques called Gradient-Based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) which allow the algorithm to run faster while maintaining a high level of accuracy. [13] LightGBM works on Linux, Windows, and macOS and supports C++, Python, [14] R, and C#. [15]
Elastix is an image registration toolbox built upon the Insight Segmentation and Registration Toolkit (ITK). [2] It is entirely open-source and provides a wide range of algorithms employed in image registration problems. Its components are designed to be modular to ease a fast and reliable creation of various registration pipelines tailored for ...
Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), [1] [2] Edge-Guided Image Interpolation (EGGI), [3] Iterative Curvature-Based Interpolation (ICBI), [citation needed] and Directional Cubic Convolution Interpolation (DCCI). [4] A study found that DCCI had the best scores in PSNR and SSIM on a series of test ...