Name
Title | ||
|---|---|---|
Orientation Histogram-Based Center-Surround Interaction: An Integration Approach for Contour Detection. |
Abstract | ||
|---|---|---|
Contour is a critical feature for image description and object recognition in many computer vision tasks. However, detection of object contour remains a challenging problem because of disturbances from texture edges. This letter proposes a scheme to handle texture edges by implementing contour integration. The proposed scheme integrates structural segments into contours while inhibiting texture edges with the help of the orientation histogram-based center-surround interaction model. In the model, local edges within surroundings exert a modulatory effect on central contour cues based on the co-occurrence statistics of local edges described by the divergence of orientation histograms in the local region. We evaluate the proposed scheme on two well-known challenging boundary detection data sets RuG and BSDS500. The experiments demonstrate that our scheme achieves a high <inline-formula><inline-graphic xlink=\"NECO_a_00911inline1.gif\" xlink:type=\"simple\"/</inline-formula>-measure of up to 0.74. Results show that our scheme achieves integrating accurate contour while eliminating most of texture edges, a novel approach to long-range feature analysis. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1162/NECO_a_00911 | Neural Computation |
Field | DocType | Volume |
Histogram,Data set,Interaction model,Object contour,Artificial intelligence,Computer vision,Image description,Pattern recognition,Methods of contour integration,Pattern recognition (psychology),Mathematics,Machine learning,Cognitive neuroscience of visual object recognition | Journal | 29 |
Issue | ISSN | Citations |
1 | 0899-7667 | 1 |
PageRank | References | Authors |
0.34 | 31 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rongchang Zhao | 1 | 30 | 4.63 |
| Min Wu | 2 | 251 | 50.43 |
| Xiyao Liu | 3 | 13 | 3.97 |
| Beiji Zou | 4 | 231 | 41.61 |
| Fangfang Li | 5 | 1 | 1.02 |