Reconstructing 3D Human Poses from Keyword Based Image Database Query
| Autor: | Mo'taz Al-Hami, Rolf Lakaemper |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Information retrieval
Matching (graph theory) business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Image (mathematics) Set (abstract data type) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Shape context Artificial intelligence business Cluster analysis Image retrieval Pose Word (computer architecture) |
| Zdroj: | 3DV |
| DOI: | 10.1109/3dv.2017.00057 |
| Popis: | The focus of this paper lies on the creation of 3D hu- man skeleton from a set of 2D images. Unlike available approaches, which utilize a single 2D image for 3D recon- struction, the prosecuted approach utilities a set of multi- ple images, which are obtained from a simple query to the google image database. We only assume, that a query key- word can be linked to a set of images, which contain a rep- resentative subset related to the query. We expect the data to also contain false (i.e. non human-pose related) images. Our approach uses a human-pose based 3D shape context model for matching human-poses in 3D space, and filter them using a hierarchical binary clustering approach. The performance of this approach is evaluated using different query keywords. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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