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Object recognition 2015 [16] [17] [18] T. Lin et al. ImageNet: Labeled object image database, used in the ImageNet Large Scale Visual Recognition Challenge: Labeled objects, bounding boxes, descriptive words, SIFT features 14,197,122 Images, text Object recognition, scene recognition 2009 (2014) [19] [20] [21] J. Deng et al. SUN (Scene ...
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.
Human Activity Recognition from wearable, object, and ambient sensors is a dataset devised to benchmark human activity recognition algorithms. None. 2551 Text Classification 2012 [189] [190] D. Roggen et al. Real World Activity Recognition Dataset Human Activity Recognition from wearable devices.
It is widely used in computer vision tasks such as image annotation, [2] vehicle counting, [3] activity recognition, [4] face detection, face recognition, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.
The recognition-by-components theory suggests that there are fewer than 36 geons which are combined to create the objects we see in day-to-day life. [3] For example, when looking at a mug we break it down into two components – "cylinder" and "handle". This also works for more complex objects, which in turn are made up of a larger number of geons.
This was used to increase the dimensionality of the set of features in an attempt to improve the detection of objects in images. This was successful, as some of these features are able to describe the object in a better way. For example, a 2-rectangle tilted Haar-like feature can indicate the existence of an edge at 45°.
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.