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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]
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 machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, [1] although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary.
An illustration of their capabilities is given by the ImageNet Large Scale Visual Recognition Challenge; this is a benchmark in object classification and detection, with millions of images and 1000 object classes used in the competition. [41] Performance of convolutional neural networks on the ImageNet tests is now close to that of humans. [41]
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 ...
Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990. [1] [2] LBP was first described in 1994.
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 ...
The way Lowe [2] determined whether a given candidate should be kept or 'thrown out' is by checking the ratio between the distance from this given candidate and the distance from the closest keypoint which is not of the same object class as the candidate at hand (candidate feature vector / closest different class feature vector), the idea is ...