The PSOM algorithm has been explained in the previous chapters. In this chapter a number of examples are presented which expose its applicability in the vision domain. Vision is a sensory information source and plays an increasingly important role as perception mechanism for robotics. The parameterized associative map and its particular completion mechanism serves here for a number of interesting application possibilities. The first example is concerned with the completion of an image feature set found here in a 2D image, invariant to translation and rotation of the image. This idea can be generalized to a set of “virtual sensors”. A redundant set of sensory information can be fused in order to improve recognition confidence and measurement precision. Here the PSOM offers a natural way of performing the fusion process in a flexible manner. As shown, this can be useful for further tasks, e.g. for inter-sensor cooperation, and identifying the the object's 3D spatial rotations and position. Furthermore, we present also a more low-level vision application. By employing specialized feature filters, the PSOM can serve for identification of landmarks in gray-scale images, here shown for fingertips.

First we want to consider here a planar example. The task is to complete a partial set of image feature locations, where the image can be translated and rotated freely. The goal is to determine the proper shift and twist angle parameters when at least two image points are seen. Furthermore we desire to predict the locations of the hidden – maybe occluded or concealed – features. For example, this can be helpful to activate and direct specialized (possibly expensive) perception schema upon the predicted region.