Name
Abstract | ||
|---|---|---|
We propose a novel multi-estimate dynamic programming (DP) method for on-line detection and segmentation of significant anomalies in a video sequence. The method is based on the concept of sparsity, which means that we reduce visual features of each frame to a set of keypoints. In our line-scan application this is done by extracting only the intensity extrema. This way, we can decrease DP's inherent noise-amplifying tendency when building up the estimates of an anomaly. For detection improving, we introduce weights that express similarity between the spatial distribution of pixels forming so-called DP score sums and a reference representing their assumed distribution. The spatial dynamics estimation is improved by 30% if compared to the intensity-only DP. Some 59% change point recovery rate is attained in a web imaging application where illumination varies, contrasts are small, and the decision making time is limited to fractions of a second due to high-speed running of the web. |
Year | Venue | Keywords |
|---|---|---|
2012 | EUSIPCO | dynamic programming,image denoising,image segmentation,image sequences,video signal processing,DP method,Web imaging,illumination,linescan application,multi-estimate dynamic programming,noise-amplifying tendency,online detection,quick online segmentation,sparsity,video sequence,visual features,Estimation,change point segmentation |
Field | DocType | ISSN |
Dynamic programming,Scale-space segmentation,Pattern recognition,Image texture,Segmentation,Computer science,Segmentation-based object categorization,Maxima and minima,Image segmentation,Pixel,Artificial intelligence | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juho Vihonen | 1 | 25 | 7.49 |
| Juhani Rauhamaa | 2 | 90 | 7.97 |
| Tommi Huotilainen | 3 | 7 | 2.62 |
| Ari Visa | 4 | 578 | 57.54 |