Many techniques for segmenting images in the absence of domain specific knowledge have been described, all with marginal success. Such an approach has been shown to be intractable. In this paper, we examine a concept bridging the gap between segmentation limitations and interpretation capabilities. In incremental segmentation, no attempt is made to obtain a complete, albeit error prone, segmentation. Instead, various heuristics are used to obtain a segmentation for the most prominent features in the image. This incomplete segmentation is forwarded to the interpretation system for initial hypothesis generation. Based on the hypotheses thus generated, the interpretation system requests the generation of additional segmentation activity to verify each hypothesis. This paper deals with determining the most prominent features in only one sense; namely those features that probably represent man-made objects in outdoor non-urban scenes. Here we provide more detail on geometric guidance. KEYWORDS: segmentation, incremental segmentation, geometric structure, line structure, 2-D description.