The primary focus of work on perceptual grouping has been geometric constraints derived from projected object boundaries. Although boundaries can be robustly extracted under some conditions, much intensity information in the image is ignored. In this work, we incorporate image topology and intensity into grouping for object detection and recognition. Using boundary-based region segmentation, region intensity, geometry and topology are exploited in forming groups of regions that satisfy high-level constraints at arbitrary scales. It is demonstrated that regions provide a natural and computationally effective framework for discovering localized image structure, such as man-made objects in natural scenes.