Introduction This paper presents a graph cut approach to the image segmentation task. Considering the image to be a directed graph with two nodes representing the source (object) and the sink (background), the authors propose a combinatorial optimization framework for image segmentation using $s/t$ graph cuts. This is the first global optimization object extraction technique that is extensible to beyond 2-D images. ...
Introduction This paper presents a novel interactive tool for efficient and reproducible boundary extraction with minimal user input. Despite the user not being very accuracte with the manual marking of the seed points, the algorithm snaps the boundary to the nearest strong object edge. Unlike active contour models where the user is unaware of how the final boundary will look like after energy minimization (which, if unsatisfactory, requires the entire process to be repeated again), this algorithm is interactive and therefore the user is aware of the “live-wire boundary snapping”. Moreover, “boundary cooling” and “on-the-fly training” are two novel contributions of the paper which help reduce user input while maintaining the stability of the boundary. ...
Introduction This paper presents a novel multi-label and interactive image segmentation algorithm that draws analogies to electric network circuits solutions from electrical engineering. Treating the image as a purely discrete object - a graph, this algorithm can be extended to surface meshes or space-variant images as well. The paper demonstrates the proposed algorithm’s speed and robustness to noise and weak boundaries while also being able to avoid the “small cut” solutions. ...
Introduction This paper presents an active contour model to detect and segment objects in images whose boundaries may or may not be defined by their gradients. The curve evolution is based on the Mumford Shah energy functional and the level set methods, making it less susceptible to curve initialization errors. ...
Introduction This paper presents a generalized technique of matching a deformable model to an image through energy minimization. An active contour model called snakes has been proposed which is a continuous and deformable spline always minimizing its energy under the influence of image forces as well as internal energy of the spline and external constraint forces. Because of its dynamic nature, snakes can be applied to image segmentation tasks (edges, lines, and subjective contours) as well as to motion tracking and stereo matching. ...