Biologically-Inspired Approach to Recognizing Dangerous Objects

For most organisms, there are dangerous objects where even a close encounter with the object could be detrimental. A visual system that helps avoid close approaches with such objects enhances survival probability beyond what is afforded by one that just facilitates simple collision avoidance. However, “dangerous object” is a category that only has meaning in a particular context, and therefore recognizing them is a very complex task. Our objective is to determine how people find dangerous objects in a continuous stream of imagery, and develop a computational implementation of the model that can be tested on imagery. Evidence is cited suggesting that the visual pathways in higher animals implement a Composable Codebook that carries out object recognition. An internal, view-independent world model stores several different types of relational information that make it possible to fill-in incomplete objects and activities once the imagery is registered to the internal world model. Interneurons play a key role in all of the filling-in processes. We illustrate how the models of the visual pathway used by Stephen Grossberg and his group to separate textures can also mediate the registration of an internal world model to visual input.