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Visual object recognition refers to the ability to identify the objects in view based on visual input. One important signature of visual object recognition is "object invariance", or the ability to identify objects across changes in the detailed context in which objects are viewed, including changes in illumination, object pose, and background context.
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.
Object recognition, scene recognition 2014 [19] [20] J. Xiao 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) [21] [22] [23] J. Deng et al. LSUN
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.
Only a modest number of geons (< 40) are assumed. When combined in different relations to each other (e.g., on-top-of, larger-than, end-to-end, end-to-middle) and coarse metric variation such as aspect ratio and 2D orientation, billions of possible 2- and 3-geon objects can be generated.
Face recognition involves configural information to process faces holistically. However, object recognition does not use configural information to form a holistic representation. Instead, each part of the object is processed independently to allow it to be recognised. This is known as a featural recognition method. [13]
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.