Name
Abstract | ||
|---|---|---|
In this paper, we deal with the problem of circle tracking across an image sequence. We propose an active contour model based on a new energy. The center and radius of the circle is optimized in each frame by looking for local minima of such energy. The energy estimation does not require edge extraction, it uses the image convolution with a Gaussian kernel and its gradient which is computed using a GPU–CUDA implementation. We propose a Newton–Raphson type algorithm to estimate a local minimum of the energy. The combination of an active contour model which does not require edge detection and a GPU–CUDA implementation provides a fast and accurate method for circle tracking. We present some experimental results on synthetic data, on real images, and on medical images in the context of aorta vessel segmentation in computed tomography (CT) images. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11554-015-0531-5 | J. Real-Time Image Processing |
Keywords | Field | DocType |
Circle, Tracking, Active Contour Models, snakes, GPU–CUDA | Active contour model,Computer vision,Computer science,Edge detection,Maxima and minima,Synthetic data,Artificial intelligence,Computed tomography,Real image,Kernel (image processing),Gaussian function | Journal |
Volume | Issue | ISSN |
14 | 4 | 1861-8219 |
Citations | PageRank | References |
3 | 0.41 | 23 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carmelo Cuenca | 1 | 16 | 6.12 |
| Esther González | 2 | 9 | 2.60 |
| Agustín Trujillo | 3 | 32 | 8.34 |
| Julio Esclarín | 4 | 35 | 4.47 |
| L. Mazorra | 5 | 38 | 6.69 |
| L. Alvarez | 6 | 285 | 39.37 |
| Juan Antonio Martínez-Mera | 7 | 5 | 1.25 |
| Pablo G. Tahoces | 8 | 127 | 16.27 |
| Jose M Carreira | 9 | 14 | 2.97 |