Recognition by Components Theory
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The Recognition-by-components theory, or RBC theory1, was proposed by Irving Biederman to explain object recognition. According to RBC theory, we are able to recognize objects by separating them into geons. Geons can be composed of various shapes (i.e. cylinders, cones, etc.) that can be assembled in various arrangements to form a virtually unlimited amount of objects.
[edit] Strengths of the theory
Utilizing geons as structural primitives results in two key advantages. Because geons are based on object properties that are stable across viewpoint, a single geon description is sufficient to describe an object from all possible viewpoints. The second advantage is that considerable economy of representation is achieved: a relatively small set of geons form a simple "alphabet" that can combine to form complex objects.
[edit] Weaknesses
RBC theory is not in itself capable of starting with a photograph of a real object and producing a geons-and-relations description of the object; the theory does not attempt to provide a mechanism to reduce the complexities of real scenes to simple geon shapes. RBC theory is also incomplete in that geons and the relations between them will fail to distinguish many real objects. For example, a pear and an apple are easily distinguished by humans but lack the corners and edges needed for RBC theory to recognize they are different. However, Irving Biederman has argued that RBC theory is the "preferred" mode of human object recognition, with a secondary process handling objects that are not distinguishable by their geons. He further states that this distinction explains research suggesting that objects may or may not be recognized equally well with changes in viewpoint².
[edit] References
Sternberg, Robert J. (2006): Cognitive Psychology. 4th Ed. Thomson Wadsworth.
1Biederman, I. (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev. 1987 Apr;94(2):115-47.
²Biederman, I. (2000). Recognizing depth-rotated objects: A review of recent research and theory. Spatial Vision, 13, 241-253.
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