Using macroscopic information in image segmentation

Post-processing `macroscopically´ output-segmented images obtained from conventional image segmentation (IS) techniques, leads into the concept of micro-macro IS (MMIS). MMIS pays extra attention to information extracted from relatively large image regions and as a result, overall system segmentation performance improves both subjectively and objectively. The proposed post-processing scheme is generic, in the sense that can be used together with any other existing segmentation approach. Thus given an input-segmented image, MMIS has the ability to automatically select an appropriate number of regions and classes in a way that helps object-oriented visual information to become more apparent in the final segmented output image. Computer simulation results clearly indicate that significant IS performance benefits can be obtained by augmenting conventional IS schemes within an MMIS framework, with or without input images being corrupted by additive Gaussian noise.