Learning Accurate Active Contours

Communications in Computer and Information Science

Volumn 383 CCIS, Issue PART 1, 2013, Pages 396-405

  Gelzinis, Adas ^a   Verikas, Antanas ^a,b   Bacauskiene, Marija ^a   Vaiciukynas, Evaldas ^a  

^a KAUNAS UNIVERSITY OF TECHNOLOGY   (Lithuania)

^b HALMSTAD UNIVERSITY   (Sweden)

Author keywords

Active contour models; Energy function; Image segmentation; Learning; Object detection; Phytoplankton images

Indexed keywords

APPLICATIONS; DECISION TREES; MODELS; PHYTOPLANKTON;

ACTIVE CONTOUR MODEL; ACTIVE CONTOURS; DETECTION TASKS; ENERGY FUNCTIONS; FOCUS OF RESEARCHES; LEARNING; OBJECT DETECTION; QUALITY ASSESSMENT;

IMAGE SEGMENTATION;

EID: 84904605140     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-41013-0_41     Document Type: Conference Paper

References (14)

1. Kass, M., Witkin, A.P., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321-331 (1988)
2. Chan, T., Vese, L.: Active contours without edges. IEEE Transactions Image Processing 10(2), 266-277 (2001)
3. Bernard, O., Friboulet, D., Thevenaz, P., Unser, M.: Variational B-spline level-set: A linear filtering approach for fast deformable model evolution. IEEE Transactions on Image Processing 18(6), 1179-1191 (2009)
4. Shang, Y., Yang, X., Zhu, L., Deklerck, R., Nyssen, E.: Region competition based active contour for medical object extraction. Computerized Medical Imaging and Graphics 32(2), 109-117 (2008)
5. Savelonas, M.A., Mylona, E.A., Maroulis, D.: Unsupervised 2D gel electrophoresis image segmentation based on active contours. Pattern Recognition 45(2), 720-731 (2012)
6. Veronese, E., Stramare, R., Campion, A., Raffeiner, B., Beltrame, V., Scagliori, E., Coran, A., Ciprian, L., Fiocco, U., Grisan, E.: Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours. Medical Engineering & Physics (2012), doi.org/10.1016/j.medengphy.2012.04.014
7. Chakraborty, A., Staib, L., Duncan, J.: Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions Medical Imaging 15(6), 859-870 (1996)
8. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61-79 (1997)
9. Ronfard, R.: Region-based strategies for active contour models. International Journal of Computer Vision 13(2), 229-251 (1994)
10. Tao, W., Tai, X.C.: Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization. Image and Vision Computing 29, 499-508 (2011)
11. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans Systems Man & Cybernetics 9(1), 62-66 (1979)
12. Verikas, A., Gelzinis, A., Bacauskiene, M.: Phase congruency-based detection of circular objects applied to analysis of phytoplankton images. Pattern Recognition 45(4), 1659-1670 (2012)
13. Breiman, L.: Random forests. Machine Learning 45, 5-32 (2001)
14. Shi, Y., Karl, W.C.: A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Transactions on Image Processing 17(5), 645-656 (2008)