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Vol.1 , No. 3, Publication Date: Oct. 27, 2014, Page: 20-28
, such as text and GPS information, information retrieval on the internet, scalable deployments of related algorithms on clustered computing architectures and multi-user interaction in social media. Object recognition has gained significant interest in scientific circles lately. Much early work on the analysis of activities took place within the computer vision community and leveraged video cameras as passive and non-invasive sensors. More recently, alternative paradigms based on dense sensors have emerged.&picture=http://www.aascit.org/aascit/decorators/img/journal/914.jpg)
| [1] | Oladosu Olakunle Abimbola, Department of Computer Technology, Yaba College of Technology, Yaba, Lagos. |
| [2] | Okikiola Folasade Mercy, Department of Computer Technology, Yaba College of Technology, Yaba, Lagos. |
| [3] | Oladiboye Olasunkanmi Esther, Department of Computer Technology, Yaba College of Technology, Yaba, Lagos. |
The availability of such imagery presents profound opportunities for image processing and computer vision research and exciting new applications in related fields. The challenge is how to organize, catalogue and retrieve such image data in a visually meaningful manner. It requires the development of new algorithms in the fields of scalable search in databases for global and/or local image features, integration with multi-modal data (meta-data), such as text and GPS information, information retrieval on the internet, scalable deployments of related algorithms on clustered computing architectures and multi-user interaction in social media. Object recognition has gained significant interest in scientific circles lately. Much early work on the analysis of activities took place within the computer vision community and leveraged video cameras as passive and non-invasive sensors. More recently, alternative paradigms based on dense sensors have emerged.
Keywords
Object, Image, Pattern, Action, Activity, Event, Metadata
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