Region-based strategies for active contour models
International Journal of Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Rapid and Brief Communication: GACV: Geodesic-Aided C-V method
Pattern Recognition
A hierarchical evolutionary algorithm for automatic medical image segmentation
Expert Systems with Applications: An International Journal
Active contour model via multi-population particle swarm optimization
Expert Systems with Applications: An International Journal
Colour, texture, and motion in level set based segmentation and tracking
Image and Vision Computing
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Expert Systems with Applications: An International Journal
Segmentation and labeling of face images for electronic documents
Expert Systems with Applications: An International Journal
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
| Hi-index | 12.05 |
A new online region-based active contour model (ORACM) is proposed in this paper. The classical geodesic active contour (GAC) model has only local segmentation property, although the Chan-Vese (C-V) model possesses global. An up-to-date active contour model (ACM with SBGFRLS) proposed in Zhang, Zhang, Song, and Zhou (2010) both has the properties of global/local segmentation and incorporates the GAC and the C-V models to raise active contours' performance on image segmentation. However it has two major disadvantages. First, it deforms the active contour model just using the gradient of current level set iteratively and so works too slowly. Second, it needs a parameter @a which plays major impact on the results and to be tuned according to input images. The proposed model ORACM eliminates these two disadvantages by using a new binary level set formula and a new regularization operation such as morphological opening and closing. Without changing segmentation accuracy, ORACM requires no parameter and less time over the traditional ACMs. Experiments on synthetic and real images demonstrate that the computational cost of ORACM with the morphological operations is 3.75 times less than the traditional ACMs on average.