## Intelligent Scissors for Image Composition (1995)

Venue: | In Computer Graphics, SIGGRAPH Proceedings |

Citations: | 235 - 6 self |

### BibTeX

@INPROCEEDINGS{Mortensen95intelligentscissors,

author = {Eric N. Mortensen and William A. Barrett},

title = {Intelligent Scissors for Image Composition},

booktitle = {In Computer Graphics, SIGGRAPH Proceedings},

year = {1995},

pages = {191--198}

}

### Years of Citing Articles

### OpenURL

### Abstract

We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary “snaps ” to, and wraps around the object of interest. Live-wire boundary detection formulates discrete dynamic programming (DP) as a two-dimensional graph searching problem. DP provides mathematically optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures. Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood. Boundary cooling automatically freezes unchanging segments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted. Extracted objects can be scaled, rotated, and composited using live-wire masks and spatial frequency equivalencing. Frequency equivalencing is performed by applying a Butterworth filter which matches the lowest frequency spectra to all other image components. Intelligent Scissors allow creation of convincing compositions from existing images while dramatically increasing the speed and precision with which objects can be extracted. 1.

### Citations

2611 |
Dynamic Programming
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- 1957
(Show Context)
Citation Context ...e minimum cost path exists from each pixel to a given seed point, many paths “coalesce” and share portions of their optimal path with paths from other pixels. Due to Bellman’s Principle of Optimality =-=[3]-=-, if any two optimal paths from two distinct pixels share a common point or pixel, then the two paths are identical from that pixel back to the seed point. This is particularly noticeable if the seed ... |

1436 | A note on two problems in connexion with graphs
- Dijkstra
- 1959
(Show Context)
Citation Context ...namic Programming As mentioned, dynamic programming can be formulated as a directed graph search for an optimal path. This paper utilizes an optimal graph search similar to that presented by Dijkstra =-=[6]-=- and extended by Nilsson [13]; further, this technique builds on and extends previous boundary tracking methods in 4 important ways: 1. It imposes no directional sampling or searching constraints. 2. ... |

828 |
Computer Vision
- Ballard, Brown
- 1982
(Show Context)
Citation Context ...son of snakes and Intelligent Scissors in section 3.6. Another class of image segmentation techniques use a graph searching formulation of DP (or similar concepts) to find globally optimal boundaries =-=[2, 4, 10, 11, 14]-=-. These techniques differ from snakes in that boundary points are generated in a stage-wise optimal cost fashion whereas snakes iteratively minimize an energy functional for all points on a contour in... |

289 |
Using dynamic programming for solving variational problems in vision
- Amini, Weymouth, et al.
- 1990
(Show Context)
Citation Context ... of region growing does not provide interactive visual feedback, resulting region boundaries must usually be edited or modified. Other popular boundary definition methods use active contours or snakes=-=[1, 5, 8, 15]-=- to improve a manually entered rough approximation. After being initialized with a rough boundary approximation, snakes iteratively adjust the boundary points in parallel in an attempt to minimize an ... |

20 |
Interactive Outlining: An Improved Approach Using Active Contours
- Daneels
- 1993
(Show Context)
Citation Context ... of region growing does not provide interactive visual feedback, resulting region boundaries must usually be edited or modified. Other popular boundary definition methods use active contours or snakes=-=[1, 5, 8, 15]-=- to improve a manually entered rough approximation. After being initialized with a rough boundary approximation, snakes iteratively adjust the boundary points in parallel in an attempt to minimize an ... |

15 |
Multiple widths yield reliable finite differences
- Fleck
- 1992
(Show Context)
Citation Context ...function. (Empirically, weights of ω Z = 0.43, ω D = 0.43, and ω G = 0.14 seem to work well in a wide range of images.) The laplacian zero-crossing is a binary edge feature used for edge localization =-=[7, 9]-=-. Convolution of an image with a laplacian kernel approximates the 2 nd partial derivative of the image. The laplacian image zero-crossing corresponds to points of maximal (or minimal) gradient magnit... |

8 |
A Decision Function Method for Boundary Detection
- Chien, Fu
- 1974
(Show Context)
Citation Context ...son of snakes and Intelligent Scissors in section 3.6. Another class of image segmentation techniques use a graph searching formulation of DP (or similar concepts) to find globally optimal boundaries =-=[2, 4, 10, 11, 14]-=-. These techniques differ from snakes in that boundary points are generated in a stage-wise optimal cost fashion whereas snakes iteratively minimize an energy functional for all points on a contour in... |

3 |
Snakes: Active
- Kass, Witkin, et al.
- 1998
(Show Context)
Citation Context ... of region growing does not provide interactive visual feedback, resulting region boundaries must usually be edited or modified. Other popular boundary definition methods use active contours or snakes=-=[1, 5, 8, 15]-=- to improve a manually entered rough approximation. After being initialized with a rough boundary approximation, snakes iteratively adjust the boundary points in parallel in an attempt to minimize an ... |