The important role of contours in visual perception has been recognized for many years (e.g., Wertheimer 1923/1938). While early Gestalt insights derive from observation of highly idealized images, decades of computer vision research have demonstrated the computational complexity of inferring and exploiting contours in natural images. Physiological data, while generating some intriguing clues, are often too local (single unit recording) or too global (imaging) to provide the data needed to constrain existing models or inspire new ones. In this talk I will discuss recent work that attempts to bring together psychophysical, computational and physiological approaches to understanding contour processing in natural images. A unifying foundation for this effort is a continuing project to measure and model the statistics of natural image contours. These ecological results lead to new computer vision algorithms for natural contour grouping, normative models for contour processing that may be evaluated psychophysically, and to new models for neural selectivity to natural image contours that may be tested against physiological data.