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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.
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.
The recognition-by-components theory, or RBC theory, [1] is a process proposed by Irving Biederman in 1987 to explain object recognition. According to RBC theory, we are able to recognize objects by separating them into geons (the object's main component parts). Biederman suggested that geons are based on basic 3-dimensional shapes (cylinders ...
Recognition of visual objects occurs at two levels. At an apperceptive level, the features of the visual information from the retina are put together to form a perceptual representation of an object. At an associative level, the meaning of an object is attached to the perceptual representation and the object is identified. [2]
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 ventral stream is associated with object recognition and form representation. Also described as the "what" stream, it has strong connections to the medial temporal lobe (which is associated with long-term memories), the limbic system (which controls emotions), and the dorsal stream (which deals with object locations and motion).
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]
The object recognition scheme uses neighboring context based voting to estimate object models. " SURF : [ 41 ] Speeded Up Robust Features" is a high-performance scale- and rotation-invariant interest point detector / descriptor claimed to approximate or even outperform previously proposed schemes with respect to repeatability, distinctiveness ...