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| Content Provider | ACM Digital Library |
|---|---|
| Author | Girisha, R. Murali, S. |
| Abstract | Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications and a robust segmentation of motion objects from the static background is generally required. Segmented foreground objects generally include their self shadows as foreground objects since the shadow intensity differs and gradually changes from the background in a video sequence. Moreover, self shadows are vague in nature and have no clear boundaries. To eliminate such shadows from motion segmented video sequences, we propose an algorithm based on inferential statistical one way ANalysis Of VAriance (ANOVA) F test. This statistical model can deal scenes with complex and time varying illuminations without restrictions on the number of light sources and surface orientations. Results obtained with different indoor and outdoor sequences show that algorithm can effectively and robustly detect associated self shadows from segmented frames. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450300018 |
| DOI | 10.1145/1754288.1754300 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-01-22 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Critical value Motion segmentation Video surveillance Anova f test Self shadows |
| Content Type | Text |
| Resource Type | Article |
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