Learning-based algorithm selection for image segmentation
Pattern Recognition Letters
optimization-based image segmentation by genetic algorithms
Journal on Image and Video Processing - Regular
Noise-robust binary segmentation based on ant colony system and modified fuzzy C-means algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Automatic red tide detection from MODIS satellite images
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Medical image processing by using soft computing methods and information fusion
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Hi-index | 0.00 |
Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.