Boundary vector field for parametric active contours
Pattern Recognition
Anisotropic virtual electric field for active contours
Pattern Recognition Letters
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
Gradient vector flow active contours with prior directional information
Pattern Recognition Letters
On the critical point of gradient vector flow snake
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A robust fully automatic scheme for general image segmentation
Digital Signal Processing
Seeded region merging based on gradient vector flow for image segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Harris function based active contour external force for image segmentation
Pattern Recognition Letters
Mean shift based gradient vector flow for image segmentation
Computer Vision and Image Understanding
| Hi-index | 0.01 |
For object segmentation, traditional snake algorithms often require human interaction; region growing methods are considerably dependent on the selected homogeneity criterion and initial seeds; watershed algorithms, however, have the drawback of over segmentation. A new downstream algorithm based on a proposed extended gradient vector flow (E-GVF) field model is presented in this paper for multiobject segmentation. The proposed flow field, on one hand, diffuses and propagates gradients near object boundaries to provide an effective guiding force and, on the other hand, presents a higher resolution of direction than traditional GVF field. The downstream process starts with a set of seeds scored and selected by considering local gradient direction information around each pixel. This step is automatic and requires no human interaction, making our algorithm more suitable for practical applications. Experiments show that our algorithm is noise resistant and has the advantage of segmenting objects that are separated from the background, while ignoring the internal structures of them. We have tested the proposed algorithm with several realistic images (e.g., medical and complex background images) and gained good results.