Learning static object segmentation from motion segmentation
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2006
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/34470 |
| _version_ | 1811081437451911168 |
|---|---|
| author | Ross, Michael G. (Michael Gregory), 1975- |
| author2 | Keslie Pack Kaelbling. |
| author_facet | Keslie Pack Kaelbling. Ross, Michael G. (Michael Gregory), 1975- |
| author_sort | Ross, Michael G. (Michael Gregory), 1975- |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. |
| first_indexed | 2024-09-23T11:46:40Z |
| format | Thesis |
| id | mit-1721.1/34470 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:46:40Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/344702019-04-12T08:52:53Z Learning static object segmentation from motion segmentation Ross, Michael G. (Michael Gregory), 1975- Keslie Pack Kaelbling. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. Includes bibliographical references (p. 105-110). This thesis describes the SANE (Segmentation According to Natural Examples) algorithm for learning to segment objects in static images from video data. SANE uses background subtraction to find the segmentation of moving objects in videos. This provides object segmentation information for each video frame. The collection of frames and segmentations forms a training set that SANE uses to learn the image and shape properties that correspond to the observed motion boundaries. Then, when presented with new static images, the model infers segmentations similar to the observed motion segmentations. SANE is a general method for learning environment-specific segmentation models. Because it is self-supervised, it can adapt to a new environment and new objects with relative ease. Comparisons of its output to a leading image segmentation algorithm demonstrate that motion-defined object segmentation is a distinct problem from traditional image segmentation. The model outperforms a trained local boundary detector because it leverages the shape information it learned from the training data. by Michael Gregory Ross. Ph.D. 2006-11-07T12:24:54Z 2006-11-07T12:24:54Z 2005 2005 Thesis http://hdl.handle.net/1721.1/34470 70717310 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 110 p. 20783141 bytes 20782946 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ross, Michael G. (Michael Gregory), 1975- Learning static object segmentation from motion segmentation |
| title | Learning static object segmentation from motion segmentation |
| title_full | Learning static object segmentation from motion segmentation |
| title_fullStr | Learning static object segmentation from motion segmentation |
| title_full_unstemmed | Learning static object segmentation from motion segmentation |
| title_short | Learning static object segmentation from motion segmentation |
| title_sort | learning static object segmentation from motion segmentation |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/34470 |
| work_keys_str_mv | AT rossmichaelgmichaelgregory1975 learningstaticobjectsegmentationfrommotionsegmentation |