
doi: 10.1007/bf01427153
The variational method has been introduced by Kass et al. (1987) in the field of object contour modeling, as an alternative to the more traditional edge detection-edge thinning-edge sorting sequence. Since the method is based on a pre-processing of the image to yield an edge map, it shares the limitations of the edge detectors it uses. In this paper, we propose a modified variational scheme for contour modeling, which uses no edge detection step, but local computations instead only around contour neighborhoods--as well as an "anticipating" strategy that enhances the modeling activity of deformable contour curves. Many of the concepts used were originally introduced to study the local structure of discontinuity, in a theoretical and formal statement by Leclerc & Zucker (1987), but never in a practical situation such as this one. The first pa~ of the paper introduces a region-based energy criterion for active contours, and gives an examination of its implications, as compared to the gradient edge map energy of snakes. Then, a simplified optimization scheme is presented, accounting for internal and external energy in separate steps. This leads to a complete treatment, which is described in the last sections of the paper (4 and 5). The optimization technique used here is mostly heuristic, and is thus presented without a formal proof, but is believed to fill a gap between snakes and other useful image representations, such as split-and-merge regions or mixed line-labels image fields.
active contours, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], video processing, 510, 004
active contours, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], video processing, 510, 004
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 442 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 0.1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
