Robust Boundary DetectionWith Adaptive Grouping

This paper presents a perceptual grouping algorithm that performs boundary extraction on natural images. Our grouping method maintains and updates a model of the appearance of the image regions on either side of a growing contour. This model is used to change grouping behaviour at run-time, so that, in addition to following the traditional Gestalt grouping principles of proximity and good continuation, the grouping procedure favours the path that best separates two visually distinct parts of the image. The resulting algorithm is computationally efficient and robust to clutter and texture. We present experimental results on natural images from the Berkeley Segmentation Database and compare our results to those obtained with three alternate grouping methods.