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The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. [1] Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.
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.
digiKam has been in development since before 2006. As of version 0.9 features include a GPS locator [9] and synchronization, [10] iPod Photo upload support, [11] an advanced metadata editor, [12] better support for raw image formats (using dcraw included in digiKam), full color management, a light-table, [13] pan-tool in Image Editor and Preview mode, improvements in usability, and many new ...
Automatic face detection with OpenCV While humans can recognize faces without much effort, [ 34 ] facial recognition is a challenging pattern recognition problem in computing . Facial recognition systems attempt to identify a human face, which is three-dimensional and changes in appearance with lighting and facial expression, based on its two ...
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
OpenCV provides a comprehensive set of functions that can support real-time computer vision applications, such as image recognition, motion tracking, and facial detection. [68] Originally developed by Intel , OpenCV has become one of the most popular libraries for computer vision due to its versatility and extensive community support.
MONAI provides a robust suite of libraries, tools, and Software Development Kits (SDKs) that encompass the entire process of building medical imaging applications. It offers a comprehensive range of resources to support every stage of developing Artificial intelligence (AI) solutions in the field of medical imaging, from initial annotation (MONAI Label), [2] through models development and ...