Escaping specularity
| Autor: | Samar M. Alsaleh, James K. Hahn, Alicia Casals, Angelica I. Aviles |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Rank (computer programming) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition 020207 software engineering 02 engineering and technology Reflectivity Image (mathematics) Specularity 0202 electrical engineering electronic engineering information engineering Specular highlight 020201 artificial intelligence & image processing Computer vision Specular reflection Artificial intelligence Focus (optics) business Surface reconstruction ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | SIGGRAPH Posters |
| DOI: | 10.1145/3102163.3102235 |
| Popis: | The appearance of objects is significantly affected by the illumination conditions in the environment. Particularly with objects that have strong reflectivity as they suffer from more dominant specular highlights, causing information loss and discontinuity in the image domain. Many computer vision algorithms are vulnerable to errors in the presence of specular highlights because they violate the image consistency assumption and hinder the performance of many vision tasks, such as object recognition, tracking and surface reconstruction [Artusi et al. 2011]. This is further complicated when we consider video sequences with free-moving cameras or dynamic objects, which is the focus of this work. |
| Databáze: | OpenAIRE |
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