Then a hypothesis was formulated about the image objects by recovering particular features. That artificial environment consisted of only regular geometrical shapes. His algorithm transformed the input image into line drawings. Then it interpreted these lines and differentiated among the pre-specified three dimensional bodies. Other machine vision approaches applied segmentation to compare the input to the pre-determined models.
Segmentation is one of the first steps of low-level image processing.
It divides the input into distinguishable units based upon a collection of common features. After the elementary regions consisting of identical pixels were identified, two heuristic operators were introduced. These attempted to merge together some of the neighboring regions based upon the assumption that their grayscale differences could be attributed to noise or uneven lightening conditions. Then the enlarged regions that formed the output of these procedures were associated with various objects and surfaces in the environment. This algorithm could be effectively used in navigation and object recognition tasks.