Tensor voting : a perceptual organization approach to computer vision and machine learning /
"This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentat...
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| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
San Rafael, Calif.
Morgan & Claypool Publishers
2007.
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| Edition: | 1st ed. |
| Series: | Synthesis lectures on image, video, and multimedia processing ;
#8. |
| Subjects: | |
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| 100 | 1 | |a Mordohai, Philippos. | |
| 245 | 1 | 0 | |a Tensor voting : |b a perceptual organization approach to computer vision and machine learning / |c Philippos Mordohai and Gerard Medioni. |
| 250 | |a 1st ed. | ||
| 264 | 1 | |a San Rafael, Calif. |b Morgan & Claypool Publishers |c 2007. | |
| 300 | |a ix, 126 pages |b illustrations |c 24 cm. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 490 | 1 | |a Synthesis lectures on image, video, and multimedia processing, |x 1559-8144 |v #8 | |
| 500 | |a Gift from APEX Knowledge Sdn. Bhd. | ||
| 504 | |a Includes bibliographical references (pages 115-123). | ||
| 520 | 1 | |a "This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources."--Jacket. | |
| 541 | |c Gift |a APEX Knowledge Sdn. Bhd. |c 2019 | ||
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Three-dimensional imaging. | |
| 700 | 1 | |a Medioni, Gerard. | |
| 830 | 0 | |a Synthesis lectures on image, video, and multimedia processing ; |v #8. |x 1559-8144 | |
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