Object recognition is a complex phenomenon, involving constant object representation while dealing with inconsistent visual stimuli. Our perception of an object's identity can endure no matter what forces act on the object in the environment. The object may be compelled to reorient itself, alter in perspective and even fleetingly disappear…
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This is simply 'object recognition'. When a lion approaches us, we realise through prior knowledge that the animal is known to be ferocious, dangerous and sometimes doesn't hesitate to kill. With this information we are able to make instant decisions as to the next course of action. A meal at a dinner table tells us it is edible and we can approach it. This 'spatial localisation' is the establishing of where objects are in the surrounding space and time, and is also an important aspect for survival. Another factor necessary for survival is 'perceptual constancy'. This is when objects, although the eyes perceive them to be in motion, are kept constant in terms of appearance. Object recognition, spatial localisation and perceptual constancy are the three main characteristics of perception.
One theory that aims to explain object recognition and constancy is Marr's theory which concerns itself with visual processing. It is also called the computational approach which involves taking two dimensional images and extracting valuable three-dimensional information from them. This theory requires examining the levels of grey in an image, creating a rough sketch, then a 2.5D sketch and representing the image as a 3D model. Marr's model of object recognition is concerned with drafting out representations of objects with increasing amount of information. The first step in this representation is creating the raw or full primal sketch. Raw primal sketches contain data regarding the light intensity variations of a shape or scene. A full primal sketch makes use of this data to determine how many outlines and objects are contained within the scene. The light intensity changes provided in the raw primal sketch gives the level of grey representation contained within the pixels of the image.
Computation on the properties shape can begin when it has become coloured i.e. detached from the background. Properties like symmetry, centre of mass, size aspect ratios are likely to offer clues as to the object's identity. The centre of mass or medial axis from the skeleton of the object is crucial to analysing the shape of the object. The use of structural primitives and spatial relationships to represent an object lies in the determination of the medial axis. This would enable us to construct a 3D model of the object. Marr explains that a 2.5D surface sketch helps to represent the visible surface. A computer vision system could reconstruct the surface with this process. Boundary detection is difficult even with advanced edge locators, but can be achieved by surface reconstruction.
The other model is Biederman's 'recognition by components' theory views all object and forms as being comprised of basic geometrical forms or 'geons'. Pattern recognition therefore is the simple identifying of these separate components. But objects need not necessarily be comprised of different components for them to be recognisable. Simple line drawings may suffice. The drawings of matchstick men, or outlines of cars or buildings, are still recognisable. Additional information such as size, colour, orientation, surface quality paints the whole picture, but it is the overall shape that is of primary importance (Biederman in Atkinson 2000, pp 164). When a silhouette of a four legged animal is shown, it is fairly easy to identify
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