Name
Abstract | ||
|---|---|---|
A novel preferential image segmentation method is proposed that performs image segmentation and object recognition using mathematical morphologies. The method preferentially segments objects that have intensities and boundaries similar to those of objects in a database of prior images. A tree of shapes is utilized to represent the content distributions in images, and curve matching is applied to compare the boundaries. The algorithm is invariant to contrast change and similarity transformations of translation, rotation and scale. A performance evaluation of the proposed method using a large image dataset is provided. Experimental results show that the proposed approach is promising for applications such as object segmentation and video tracking with cluttered backgrounds. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TIP.2008.2010202 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
cluttered background,method preferentially segments object,image segmentation,novel preferential image segmentation,object segmentation,prior image,object recognition,large image dataset,detectors,video tracking,algorithm design and analysis,mathematical morphology,morphology,shape,similarity transformation | Object detection,Computer vision,Scale-space segmentation,Pattern recognition,Image texture,Range segmentation,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Mathematics,Minimum spanning tree-based segmentation | Journal |
Volume | Issue | ISSN |
18 | 4 | 1057-7149 |
Citations | PageRank | References |
2 | 0.37 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongsheng Pan | 1 | 29 | 4.54 |
| J. Douglas Birdwell | 2 | 59 | 10.38 |
| Seddik M. Djouadi | 3 | 216 | 42.08 |