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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.
[1] [2] He is best known for his seminal work in facial recognition and machine learning. He is the co-inventor of the Viola–Jones object detection framework along with Michael Jones. [3] [4] He won the Marr Prize in 2003 and the Helmholtz Prize from the International Conference on Computer Vision in 2013. [5]
The position of these rectangles is defined relative to a detection window that acts like a bounding box to the target object (the face in this case). In the detection phase of the Viola–Jones object detection framework, a window of the target size is moved over the input image, and for each subsection of the image the Haar-like feature is ...
To search for the object in the entire frame, the search window can be moved across the image and check every location with the classifier. This process is most commonly used in image processing for object detection and tracking, primarily facial detection and recognition. The first cascading classifier was the face detector of Viola and Jones ...
Paul Viola and Michael Jones combined their face detection method with the Haar-like feature approach to object recognition in digital images to launch AdaBoost, the first real-time frontal-view face detector. [29] By 2015, the Viola–Jones algorithm had been implemented using small low power detectors on handheld devices and embedded systems.
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