Imaging sensors produce images whose primary use is to convey information to human operators. However, their proliferation has resulted in an overload of information. As a result, computational algorithms are being increasingly implemented to simplify an operator's task or to eliminate the human operator altogether. Predicting the effect of algorithms on task performance is currently cumbersome requiring estimates of the effects of an algorithm on the blurring and noise, and “shoe-horning” these effects into existing models. With the increasing use of automated algorithms with imaging sensors, a fully integrated approach is desired. While specific implementation algorithms differ, general tasks can be identified that form building blocks of a wide range of possible algorithms. Those tasks are segmentation of objects from the spatio-temporal background, object tracking over time, feature extraction, and transformation of features into human usable information. In this paper research is conducted with the purpose of developing a general performance model for segmentation algorithms based on image quality. A database of pristine imagery has been developed in which there is a wide variety of clearly defined regions with respect to shape, size, and inherent contrast. Both synthetic and “natural” images make up the database. Each image is subjected to various amounts of blur and noise. Metrics for the accuracy of segmentation have been developed and measured for each image and segmentation algorithm. Using the computed metric values and the known values of blur and noise, a model of performance for segmentation is being developed. Preliminary results are reported.