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The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009.
DNN Platform (formerly "DotNetNuke Community Edition" content management system) is open source software distributed under an MIT License that is intended to allow management of websites without much technical knowledge, and to be extensible through a large number of third-party apps to provide functionality not included in the DNN core modules.
This template is transcluded in DNN (software) and List of content management systems. The current DNN (software) stable version is 9.13.6 [ 1 ] , released on 2024-11-19 [±] . ^ a b "DNN Platform Release Notes" .
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.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
LeNet-5 architecture (overview). LeNet is a series of convolutional neural network structure proposed by LeCun et al.. [1] The earliest version, LeNet-1, was trained in 1989.In general, when "LeNet" is referred to without a number, it refers to LeNet-5 (1998), the most well-known version.
The process of Canny edge detection algorithm can be broken down to five different steps: Apply Gaussian filter to smooth the image in order to remove the noise; Find the intensity gradients of the image