Synergistic arc-weight estimation for interactive image segmentation using graphs
We introduce a framework for synergistic arc-weight estimation, where the user draws markers inside each object (including background), arc weights are estimated from image attributes and object information (pixels under the markers), and a visual feedback guides the users next action. We demonstrate the method in several graph-based segmentation approaches as a basic step (which should be followed by some proper approach-specific adaptive procedure) and show its advantage over methods that do not exploit object information and over methods that recompute weights during delineation, which make the user to lose control over the segmentation process. We also validate the method using medical data from two imaging modalities (CT and MRI-T1).
image foresting transform graph-cut segmentation relative-fuzzy connectedness watershed transform live-wire segmentation contour tracking interactive segmentation graph-search algorithms ? ? -connected segmentation ? ? ? ? ?