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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.
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 calculated. This difference is then compared to a learned threshold that separates non-objects from objects.
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] He is the holder of at least 57 patents in the areas of advanced machine learning, web search, data mining, and image ...
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 ...
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Most computer vision and machine learning algorithms function by training on example inputs. They require a large and varied set of training data to work effectively. For example, the real-time face detection method used by Paul Viola and Michael J. Jones was trained on 4,916 hand-labeled faces.
Viola–Jones object detection framework This page was last edited on 27 July 2023, at 15:52 (UTC). Text is available under the Creative Commons Attribution ...
KLT is not an algorithm used for face detection. It is a feature tracking algorithm, plain and simple. The source provided is a MATLAB demo that uses KLT to track features in a bounding box found *after* face detection is performed separately using Viola-Jones. I am modifying this section accordingly. Marcman411 14:04, 19 August 2017 (UTC)