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ABBYY also supplies SDKs for embedded and mobile devices. Professional, Corporate and Site License Editions for Windows, Express Edition for Mac. [3] AIDA: 2016 13.0 2024 Proprietary: Yes Yes Yes Yes Yes Yes Yes No All languages using Latin alphabet Machine and handprinted text, Latin alphabet DOCX, XLSX, PPTX, TXT, CSV, PDF, JSON, XML
On average only 0.01% of all sub-windows are positive (faces) Equal computation time is spent on all sub-windows; Must spend most time only on potentially positive sub-windows. A simple 2-feature classifier can achieve almost 100% detection rate with 50% FP rate. That classifier can act as a 1st layer of a series to filter out most negative windows
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.
Implementation of Otsu's thresholding method as GIMP-plugin using Script-Fu (a Scheme-based language) Lecture notes on thresholding – covers the Otsu method; A plugin for ImageJ using Otsu's method to do the threshold; A full explanation of Otsu's method with a working example and Java implementation; Implementation of Otsu's method in ITK
Facial recognition software at a US airport Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station. A facial recognition system [1] is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.
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. [2] CVAT supports the primary tasks of supervised machine learning: object detection, image classification, and image segmentation. CVAT allows users to annotate data for each of ...
FindFace employs a facial recognition neural network [6] algorithm developed by N-Tech.Lab [7] [8] to match faces in the photographs uploaded by its users against faces in photographs published on VK, [9] with a reported accuracy of 70 percent. [10] Different sources point to NTech Lab's technology accuracy from 85.081% [11] to 99%. [12]
Windows, Linux, macOS: Java, Python: Swing: Open core: Full version under Apache License 2.0: Yes Yes Yes Unknown Yes Yes (full version only) Yes (full version only) Yes Yes PEP 8 and others Yes Yes Yes Yes Yes PyDev / LiClipse (plug-in for Eclipse and Aptana) Appcelerator: 7.5.0 2020-01-10 Windows, Linux, macOS, FreeBSD, JVM, Solaris: Python ...