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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.
vott.z22.web.core.windows.net VoTT ( V isual O bject T agging T ool) is a free and open source Electron app for image annotation and labeling developed by Microsoft . [ 1 ] The software is written in the TypeScript programming language and used for building end-to-end object detection models from image and videos assets for computer vision ...
A raster graphics editor (also called bitmap graphics editor) is a computer program that allows users to create and edit images interactively on the computer screen and save them in one of many raster graphics file formats (also known as bitmap images) such as JPEG, PNG, and GIF.
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.
If the user needs to add annotations, highlightings or obfuscations to the screenshot the built-in image editor can be used. Greenshot's image editor is a basic vector graphics editor; however, it offers some pixel-based filters. It allows to draw basic shapes (rectangles, ellipses, lines, arrows and freehand) and add text to a screenshot.
ELAN is computer software, a professional tool to manually and semi-automatically annotate and transcribe audio or video recordings. [2] It has a tier-based data model that supports multi-level, multi-participant annotation of time-based media.
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The LabelMe annotation tool provides a means for users to contribute to the project. The tool can be accessed anonymously or by logging into a free account. To access the tool, users must have a compatible web browser with JavaScript support. When the tool is loaded, it chooses a random image from the LabelMe dataset and displays it on the screen.