Name
Abstract | ||
|---|---|---|
The framework of our proposed for segmenting objects using spatial location information from stereo images. An efficient graph-based image segmentation algorithm within this framework for combining changes in optical features and physical location to segment reality scenes into perceptually and semantically uniform regions. Optical and physical location are extracted using k-means clustering, and we propose a rules table for combining optical and spatial features together. The performance of our proposed framework is demonstrated in a series of reality-scene images using experimental data from the Middlebury stereo image data (http://vision.middlebury.edu/stereo/data/). |
Year | DOI | Venue |
|---|---|---|
2010 | 10.20965/jaciii.2010.p0645 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
object segmentation, image segmentation, stereo image, minimum spanning tree clustering | Computer vision,Scale-space segmentation,Pattern recognition,Range segmentation,Segmentation,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Minimum spanning tree-based segmentation | Journal |
Volume | Issue | ISSN |
14 | 6 | 1343-0130 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Hao | 1 | 0 | 0.68 |
| Lifeng He | 2 | 441 | 40.97 |
| Tsuyoshi Nakamura | 3 | 18 | 8.61 |
| Yuyan Chao | 4 | 315 | 24.07 |
| Hidenori Itoh | 5 | 368 | 252.31 |