Simple line drawings and 2D sketches are commonly used by humans to convey their ideas about a particular shape or shapes in an image. These approximations of shapes are effective means for visual communication and artistic practices. The idea of shape abstraction can be derived from such approximations of shapes, which considers their most important and salient features. The key idea behind shape abstraction is to extract a simplified version of a shape that preserves the salient characteristics of the input shape. In this paper, we introduce and analyze a slightly different and novel facet of abstraction, which we call “partial to full shape recognition” of two dimensional shapes (line drawing and sketches). The key idea is recognizing partial 2D shapes that leads to recognition of full shape utilizing the theory of recognition-by-components (RBC) and geons (human shape perception). We segment the 2D shapes according to the non-accidental relations provided by RBC and analyze the electroencephalogram (EEG) brain activity of subjects using a brain computer interface (BCI) to gain knowledge of human understanding of such relations pertaining to specific partial to full shape correspondence.

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