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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.
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] Well-researched domains of object detection include face detection and pedestrian detection.
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).
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 ...
In humans, areas specialized for visual object recognition in the ventral stream have a more inferior location in the temporal cortex, whereas areas specialized for the visual-spatial location of objects in the dorsal stream have a more superior location in the parietal cortex. However, these two streams hypothesis, although useful, are a ...
Template matching theory describes the most basic approach to human pattern recognition. It is a theory that assumes every perceived object is stored as a "template" into long-term memory. [4] Incoming information is compared to these templates to find an exact match. [5]
In a similar way, certain particular patches and regions of the cortex are more involved in face recognition than other object recognition. Some studies tend to show that rather than the uniform global image, some particular features and regions of interest of the objects are key elements when the brain needs to recognise an object in an image.