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AForge.NET is a computer vision and artificial intelligence library originally developed by Andrew Kirillov for the .NET Framework. [2]The source code and binaries of the project are available under the terms of the Lesser GPL and the GPL (GNU General Public License).
SimpleITK is a simplified, open-source interface to the Insight Segmentation and Registration Toolkit (ITK). The SimpleITK image analysis library is available in multiple programming languages including C++, Python, R, [1] Java, C#, Lua, Ruby and Tcl.
Images Railway signal recognition 2023 [67] [68] Philipp Leibner, Fabian Hampel, Christian Schindler Multi-cue pedestrian Multi-cue onboard pedestrian detection dataset is a dataset for detection of pedestrians. The databaset is labeled box-wise. 1092 image pairs with 1776 boxes for pedestrians Images Object recognition and classification 2009 [69]
Depop's new AI feature generates item descriptions using image-recognition technology. It's boosted the number of site listings and saves users time. How Depop's AI image-recognition tool speeds ...
This comparison of optical character recognition software includes: . OCR engines, that do the actual character identification; Layout analysis software, that divide scanned documents into zones suitable for OCR
The Open Neural Network Exchange (ONNX) [ˈɒnɪks] [2] is an open-source artificial intelligence ecosystem [3] of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector.
The application is a visual search engine application that utilizes image recognition to photograph, identify, and provide information on any object, at any angle. Its image recognition capabilities make use of CloudSight API. [8] CamFind surpassed 1,000,000 downloads within the first seven months after its release into the Apple AppStore. [9]
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]