Real Time Interactive Image Segmentation Using User Indicated Real-World Seeds

We propose a novel and fast interactive segmentation methodology for computer vision applications. Basically, the proposed system performs the tracking of seeds so that multiple seeds can be acquired over time, substantially improving the segmentation results. Moreover, instead of image coordinates, the user indicates points in the real-world that become seeds in the image. These seeds can be indicated, for example using a laser pointer or a smart-phone. The seeds can then be tracked and used by a segmentation algorithm. Experiments using the Lucas-Kanade Optical Flow and the Fast Multi-Object Fuzzy Segmentation (Fast-MOFS) algorithm demonstrate that the proposed technique successfully segments images in real-time and improves the user ability to directly segment an object in the real world. The proposed system has a high performance, allowing it to be used with high frame rates in devices with low processing capability and/or with restricted power requirements.