A model for the perception of curves in dot figures: the role of local salience of "virtual lines".

In many models of visual information processing the notion of a virtual line or dipole is introduced in order to represent the configurational information, notably length and relative orientation, between identical figure elements in figures with discrete elements. Virtual lines have proven to be very useful in predicting perceptual phenomena (Julesz et al. 1973; Stevens 1978). In the present study, virtual lines are utilized in a model which aims to predict the perception of (dotted) curves in dot figures. Clearly many possible curves, formed by adjacent virtual lines, can be constructed within a set of dots. It is proposed that already at the local level of the virtual lines each line has a perceptual salience which results from the function induced by the global dot figure. It is this local line salience or "connectivity" that directs further processing and determines the curves to be seen in a dot figure. The model presented is an information processing model with a clear modular design. It entails three successive levels of representation. First image functions are derived through a convolution of the input with gaussian distribution functions. Next, a discrete internal representation is extracted from the image function consisting of two primitives; blobs, representing the dots, and virtual lines, representing pairwise relations between blobs. The attributes of the blobs are their positions in the image plane, while those of the virtual lines are length, relative orientation and connectivity. At the third level, the discrete internal representation is used to predict the perceived curves. It is shown that the model has advantages over other approaches, e.g. autocorrelation and network models.