10.6Recapitulation

We have described simple shapes by remembering their contour propagation pattern as viewed through a set of orientation columns. The resulting vector field - called the contour propagation field, CPF - describes the inside and outside of shape (expressed in figure 58). A neural substrate storing this CPF can properly recognize learned shapes, even if they are scaled in size, translated in position and degraded in various manners. The system learns a novel shape with a single ‘shot’ using an ‘unwritten’ map for each new shape. The CPF matching system can deal with simple shapes, that are structurally not too similar. We have mentioned several possibilities to extend the system, like operating on different axes (local/global) or by creating a differentiation mechanism. If such a system can be developed for basic-level objects, it has the potential to work purely bottom-up despite the persistent presence of noise in the contour image, and it has the potential to recognize several objects simultaneously, even if they partially overlap.

Figure 58: The idea of using a CPF for representation is expressed with a set of arrows indicating the direction of waves.

In search of an expansion to a basic-level categorization system, one would firstly attempt to succeed on line-drawings like the ones in chapter 5. Such line-drawing objects could include a substantial amount of variability - more then the ones in chapter 5 -, because the present CPF system is already able to deal with a significant level of variability. Ideally, in a second step, one would design a learning system that can acquire the necessary regions by a saccadic visual search. Then, one would elaborate the system to perform on gray-

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