Automatic landmark correspondence detection for ImageJ

Landmark correspondences can be used for various tasks in image processing such as image alignment, reconstruction of panoramic photographs, object recognition and simultaneous localization and mapping for mobile robots. The computer vision community knows several techniques for extracting and pairwise associating such landmarks using distinctive invariant local image features. Two very successful methods are the Scale Invariant Feature Transform (SIFT) and Multi-Scale Oriented Patches (MOPS). We implemented these methods in the Java programming language for seamless use in ImageJ. We use it for fully automatic registration of gigantic serial section Transmission Electron Microscopy (TEM) mosaics. Using automatically detected landmark correspondences, the registration of large image mosaics simplifies to globally minimizing the displacement of corresponding points. We present here an introduction to automatic landmark correspondence detection and demonstrate our implementation for ImageJ. We demonstrate the application of the plug-in on diverse image data.