Name
Abstract | ||
|---|---|---|
Small surface objects, usually containing important information, are difficult to be identified under realistic atmospheric conditions because of weather degraded image features. This paper describes a novel algorithm to overcome the problem, using depth-aware analysis. Because objects-participating local patches always contain low intensities in at least one color channel, we detect suspicious small surface objects using the dark channel prior. Then, we estimate the approximate depth map of maritime scenes from a single image, based on the theory of perspective projection. Finally, using the estimated depth map and the atmospheric scattering model, we design spatial-variant thresholds to identify small surface objects from noisy backgrounds, without contrast enhancement. Experiments show that the proposed method has real-time implementation, and it can outperform the state-of-the-art algorithms on the detection of distant small surface objects with only a few pixels. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-19318-7_3 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
realistic atmospheric condition,atmospheric scattering model,dark channel,weather effect,color channel,suspicious small surface object,estimated depth map,approximate depth map,small surface object,distant small surface object,real-time detection,single image,perspective projection,depth map,real time,image features | Computer vision,Pattern recognition,Diffuse sky radiation,Feature (computer vision),Computer science,Communication channel,Perspective (graphical),Artificial intelligence,Pixel,Depth map,Channel (digital image) | Conference |
Volume | Issue | ISSN |
6494 LNCS | PART 3 | 16113349 |
Citations | PageRank | References |
1 | 0.47 | 15 |
Authors | ||
4 | ||