Estimation of object class and orientation from multiple viewpoints and relative camera orientation constraints
| Autor: | Keita Iseki, Koichi Ogawara |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Class (computer programming)
Orientation (computer vision) business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Camera orientation Object (computer science) Viewpoints Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Object Class 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Humanoid robot Interpolation |
| Zdroj: | IROS |
| DOI: | 10.1109/iros45743.2020.9340771 |
| Popis: | In this research, we propose a method of estimating object class and orientation given multiple input images assuming the relative camera orientations are known. Input images are transformed to descriptors on 2-D manifolds defined for each class of object through a CNN, and the object class and orientation that minimize the distance between input descriptors and the descriptors associated with the estimated object class and orientation are selected. The object orientation is further optimized by interpolating the viewpoints in the database.The usefulness of the proposed method is demonstrated by comparative evaluation with other methods using publicly available datasets. The usefulness of the proposed method is also demonstrated by recognizing images taken from the cameras on our humanoid robot using our own dataset. |
| Databáze: | OpenAIRE |
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