Building representations of proto-objects with exploration of the effect on fixation times

During development, infants rapidly build models of the world around them, segmenting the visual scene into clusters of features that can be indexed as proto-objects. These proto-objects form the foundation of more specialised object perception later on, but also act as a means for generalising, comparing and recognising similar objects. This paper takes inspiration from psychological studies to present an approach for building representations of proto-objects that can be learned on-line on a robotic platform and used for object recognition. In particular, from our previous studies of infant visual development, we first identify four types of features; brightness, motion, colour and edges, and then apply heuristics to cluster them into proto-object representations. When correlations of the observed features are made, pairs of features are used to construct graphs that encapsulate information of the observed phenomena. By a three-phase experiment we demonstrate the robot's ability of effectively learn proto-object representations and then, by utilising the graphs, to recognise what is presented to it and report on the impact uncertainties in object recognition have on fixation times.