Name
Abstract | ||
|---|---|---|
We utilize outlier detection by principal component analysis (PCA) as an effective step to automate snakes/active contours for object detection. The principle of our approach is straightforward: we allow snakes to evolve on a given image and classify them into desired object and non-object classes. To perform the classification, an annular image band around a snake is formed. The annular band is considered as a pattern image for PCA. Extensive experiments have been carried out on oil-sand and leukocyte images and the performance of the proposed method has been compared with two other automatic initialization and two gradient-based outlier detection techniques. Results show that the proposed algorithm improves the performance of automatic initialization techniques and validates snakes more accurately than other outlier detection methods, even when considerable object localization error is present. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/LSP.2009.2017477 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
image classification,object detection,principal component analysis,PCA,automate snakes/active contours,automatic initialization,gradient-based outlier detection techniques,leukocyte images,object detection,outlier detection method,principal component analysis,snake validation,Active contour,classification,principal component analysis,snake | Active contour model,Anomaly detection,Computer vision,Object detection,Pattern recognition,Computer science,Artificial intelligence,Initialization,Contextual image classification,Principal component analysis | Journal |
Volume | Issue | ISSN |
16 | 6 | 1070-9908 |
Citations | PageRank | References |
5 | 0.46 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baidya Nath Saha | 1 | 59 | 7.95 |
| Ray Nilanjan | 2 | 541 | 55.39 |
| Hong Zhang | 3 | 582 | 74.33 |