Boundary snapping for robust image cutouts

Boundary snapping is an interactive image cutout algorithm that requires a small number of user supplied control points, or landmarks, to infer the cutout contour. The key idea is to match the appearance of all points along the desired contour to the landmark points, where appearance is given by an intensity profile perpendicular to the boundary. An optimization process attempts to find a contour that maximizes the similarity score of its points with the landmarks. This approach works well in the typical case where the foreground and background differ in appearance, as well as in challenging cases where the subject is clearly perceived, but the regions on both sides of the boundary are similar and cannot be easily discriminated. By enabling the user to define the boundary points directly, the technique is not limited to boundaries that necessarily have to be the most salient or high gradient feature in the region. It can also be used for margin cutout around the boundary. The use of multiple control points along the boundary can handle spatially varying attributes as both foreground and background may change in appearance along the boundary. The final result is accurate, because it allows the user to enforce hard constraints on the boundary directly, at the expense of moderate user labor in positioning the landmark points. Finally, the algorithm is fast, works on a variety of images, and handles situations where the boundary is not obvious.

[1]  Michael Gleicher,et al.  This document was created with FrameMaker 4.0.4 Image Snapping , 2022 .

[2]  William A. Barrett,et al.  Image Editing with Intelligent Paint , 2002, Eurographics.

[3]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.

[4]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[5]  David Salesin,et al.  Interactive digital photomontage , 2004, ACM Trans. Graph..

[6]  Tao Zhang,et al.  Interactive graph cut based segmentation with shape priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Patrick Pérez,et al.  JetStream: probabilistic contour extraction with particles , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[9]  David Salesin,et al.  Interactive digital photomontage , 2004, SIGGRAPH 2004.

[10]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[11]  William A. Barrett,et al.  Object-based image editing , 2002, ACM Trans. Graph..

[12]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[14]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[15]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  William A. Barrett,et al.  Toboggan-based intelligent scissors with a four-parameter edge model , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[17]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.