Name
Abstract | ||
|---|---|---|
Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TIP.2005.852199 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
nontextured object,spatial data structures,biomedical image processing,map,tensor,random processes,bayesian framework,maximum likelihood estimation,image segmentation,isointensity structural orientation,maximum aposteriori estimation,hessian matrices,markov processes,maximum a posteriori (map) estimation,bayes methods,markov random field,computer vision,stochastic field,image texture,stochastic fields,tensors,multilevel logistic mrf model,piecewise homogeneous assumption | Computer vision,Scale-space segmentation,Pattern recognition,Image texture,Segmentation,Image quality,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Thresholding,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 10 | 1057-7149 |
Citations | PageRank | References |
23 | 1.23 | 43 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wilbur C. K. Wong | 1 | 110 | 8.45 |
| Albert C. S. Chung | 2 | 964 | 72.07 |