A biologically inspired method to encode the shape of enclosed image regions is presented. Shape encoding units, called "shape cells", are algebraic filters which receive two-dimensional (2-D) reconstructed image boundaries as input features. Shape cell outputs are calculated as three components. First, a compact description of an image boundary shape surrounding a particular shape cell is obtained in the form of a shape cell centered radial scanning vector. Shape cell activations are then calculated from radial scans, and finally, different shape cells that encode parts of the same shape must be grouped together in ensembles. This process is called feature binding. A process of iterative lateral inhibition is employed to condense the set of active shape cells before feature binding takes place. The output radial scanning vectors of shape cells provide compact descriptions of shape which are useful in object identification. The spatial pattern (2-D coordinates) of active shape cells can be used in object localization. With feature binding separate ensembles are created, even if neighboring shapes are only divided by weak boundaries. Besides its application in pattern recognition, shape encoding provides a possible mechanism of figure-ground separation. Artificial shape encoding is further concluded to be a suitable addendum to the existing collective model of biological vision.