The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N ^ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermmation. The detection of these properties is generally invariant over viewing position and image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configurations to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimensional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but instead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its particular category. But it is only with rare exceptions that an image fails to be rapidly and readily classified, either as an instance of a familiar object category or as an instance that cannot be so classified (itself a form of classification). Consider the object shown in Figure 1 . We readily recognize it as one of those objects that cannot be classified into a familiar category. Despite its overall unfamiliarity, there is near unanimity in its descriptions. We parse-or segment-its parts at regions of deep concavity and describe those parts with common, simple volumetric terms, such as "a block," "a cylinder" "a funnel or truncated cone." We can look at the zig-zag horizontal brace as a texture region or zoom in and interpret it as a series of connected blocks. The same is true of the mass at the lower left: we can see it as a texture area or zoom in and parse it into its various bumps. Although we know that it is not a familiar object, after a while we can say what it resembles: "A New York City hot dog cart, with the large block being the central food storage and cooking area, the rounded part underneath as a wheel, the large arc on the right as a handle, the funnel as an orange juice squeezer and the various vertical pipes as vents or umbrella supports." It is not a good cart, but we can see how it might be related to one. It is like a 10-letter word with 4 wrong letters. We readily conduct the same process for any object, familiar or unfamiliar, in our foveal field of view. The manner of segmentation and analysis into components does not appear to depend on our familiarity with the particular object being identified. The naive realism that emerges in descriptions of nonsense objects may be reflecting the workings of a representational system by which objects are identified.
Classical visual theories like Biederman's propose that objects can be divided in their constituent components, and so Recognition By Components (RBCs) can be achieved REF .
Recognition-by-components: A theory of human image understanding
Psychological Review
Mathematics Medicine