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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Savarese, S. Li Fei-Fei |
| Copyright Year | 2007 |
| Description | Author affiliation: Priceton Univ., Princeton (Li Fei-Fei) || Univ. of Illinois, Urbana (Savarese, S.) |
| Abstract | We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotations and scale changes. Our approach is to capture a compact model of an object category by linking together diagnostic parts of the objects from different viewing points. We emphasize on the fact that our "parts" are large and discriminative regions of the objects that are composed of many local invariant features. Instead of recovering a full 3D geometry, we connect these parts through their mutual homographic transformation. The resulting model is a compact summarization of both the appearance and geometry information of the object class. We propose a framework in which learning is done via minimal supervision compared to previous works. Our results on categorization show superior performances to state-of-the-art algorithms such as (Thomas et al., 2006). Furthermore, we have compiled a new 3D object dataset that consists of 10 different object categories. We have tested our algorithm on this dataset and have obtained highly promising results. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 3982816 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424416301 |
| ISSN | 15505499 |
| DOI | 10.1109/ICCV.2007.4408987 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-14 |
| Publisher Place | Brazil |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Solid modeling Information geometry Testing Shape Object recognition Computer science Joining processes Encoding Algorithm design and analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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