Name
Abstract | ||
|---|---|---|
This paper presents a simple approach for detecting salient regions in stereo images. The approach computes saliency by considering three factors: disparity influence, central bias and spatial dissimilarity. Firstly, an image is split into equal-sized patches to be downsampled. Next, disparity influence is estimated based on the disparity map. Besides, central bias value is assigned to every patch and spatial dissimilarity is measured between patches in reduced dimensional space. Thereafter, the product of all factors extracted from the image is computed. Finally, through a process of normalization, the saliency map is obtained. In the experiments four state-of-the-art methods are used for comparison with PSU stereo saliency benchmark dataset (SSB). The experimental results show that our method has better performance than the others for stereo salient region detection. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-77383-4_25 | ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2017, PT II |
Keywords | Field | DocType |
Stereo,Saliency,Disparity influence,Central bias,Spatial dissimilarity | Computer vision,Saliency map,Normalization (statistics),Pattern recognition,Salience (neuroscience),Computer science,Artificial intelligence,Region detection,Salient | Conference |
Volume | ISSN | Citations |
10736 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lijuan Duan | 1 | 2 | 2.39 |
| Fangfang Liang | 2 | 9 | 1.43 |
| Wei Ma | 3 | 12 | 1.24 |
| Shuo Qiu | 4 | 3 | 3.14 |