This paper introduces a general purpose scene segmentation system based on the model that the gradient value at region borders exceeds the gradient within regions. All internal and external parameters are identified and discussed, and the methods of selecting their values are specified. User-provided external parameters are based on segmentation scale: the approximate number of regions (within 50%) and typical perimeter:area ratio of objects of interest. Internal variables are assigned values adaptively, based on image data and the external parameters. The algorithm for region formation combines detected edges and a classical region growing procedure which is shown to perform better than either method alone. A confidence measure in the result is provided automatically, based on the match of the actual segmentation to the original model. Using this measure, there is confirmation whether or not the model and the external parameters are appropriate to the image data. This system is tested on many domains, including aerial photographs, small objects on plain and textured backgrounds, CT scans, stained brain tissue sections, white noise only and laser range images. The system is intended to be applied as one module in a larger vision system. The confidence measure provides a means to integrate the result of this segmentation and segmentations based on other modules. This system is also internally modular, so that another segmentation algorithm or another region formation algorithm could be included without redesigning the entire system.