The human visual system can detect many classes of patterns and statistically significant arrangements of image elements. Perceptual grouping refers to the human visual ability to extract significant image relations from lower-level primitive image features without any knowledge of the image content and group them to obtain meaningful higher-level structure. Research in perceptual grouping was started in 1920's by Gestalt psychologists, whose goal was to discover the underlying principle that would unify the various grouping phenomena of human perception. Gestalt psychologists observed the tendency of the human visual system to perceive configurational wholes, with rules that govern the uniformity of psychological grouping for perception and recognition, as opposed to recognition by analysis of discrete primitive image features. The hierarchical grouping principles proposed by Gestalt psychologists embodied such concepts as grouping by proximity, similarity, continuation, closure, and symmetry [10].
The grouping of low-level features provides a higher-level structure. These higher-level structures may be further combined to yield another level of higher-level structures. The process may be repeated until a meaningful semantic representation is achieved that may be used by a higher-level reasoning process. Certain scene structures will always produce images with discernable features regardless of viewpoint, while other scene structures virtually never do. This correlation between salience and invariance has suggested that the perceptual salience of viewpoint invariance is due to the leverage it provides for inferring geometric properties of objects and scenes. It has been noted that many of the perceptually salient image properties identified by the Gestalt psychologists such as collinearity, parallelism, and good continuation, are viewpoint invariant [11].
To discover and describe structure, the visual system uses a wide array of perceptual grouping mechanisms. These range from the relatively low-level mechanisms that underlie the simplest principles of grouping and segregation, to relatively high-level mechanisms in which complex learned associations guide the discovery of structure. Perceptual grouping generally results in highly compact representations of images, facilitating later processing, storage, and retrieval [12].
Many computer vision systems implicitly use some aspects of processing that can be directly related to the perceptual grouping processes of the human visual system[13]. Frequently, however, no claim is made about the pertinence or adequacy of the digital models as embodied by computer algorithms to the proper model of human visual perception [14]. Edge-linking and region-segmentation, which are used as structuring processes for object recognition, are seldom considered to be a part of an overall attempt to structure the image [13]. This enigmatic situation arises because research and development in computer vision is often considered quite separate from research into the functioning of human vision. A fact that is generally ignored is that biological vision is currently the only measure of the incompleteness of the current stage of computer vision, and illustrates that the problem is still open to solution [10].