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All I Have Seen (AIHS)
Fusion Box
Autonomous Driving
|...
许可协议: Unknown

Overview

The All I Have Seen (AIHS) dataset is created to study the properties of total visual input in humans, for around two weeks Nebojsa Jojic wore a camera capturing, on average, an image per every 20 seconds of his waking hours. The resulting new dataset contains a mix of indoor and outdoor scenes as well as numerous foreground objects.The creators first analysis goal is to create a visual summary of the subjects two weeks of life using unsupervised algorithms that would automatically discover recurrent scenes, familiar faces or common actions. Direct application of existing algorithms, such as panoramic stitching (e.g. Photosynth) or appearance-based clustering models (e.g. the epitome), is impractical due to either the large dataset size or the dramatic variation in the lighting conditions.The authors dubbed this type of data "All I have Seen" (AIHS, meant to be pronounced similar to "eyes"). While these types of datasets have been assembled before, it is our belief that with the proliferation of mobile devices and the availability of cloud computing, the time is now more appropriate than ever for research into this type of data acquisition, unsupervised techniques for data analysis and applications on top of them.Structural epitome: a way to summarize ones visual experienceNebojsa Jojic and Alessandro Perina and Vittorio MurinoNIPS 2010

数据概要
数据格式
video, point cloud,
数据量
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文件大小
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| 数据量 -- | 大小 --
All I Have Seen (AIHS)
Fusion Box
Autonomous Driving
许可协议: Unknown

Overview

The All I Have Seen (AIHS) dataset is created to study the properties of total visual input in humans, for around two weeks Nebojsa Jojic wore a camera capturing, on average, an image per every 20 seconds of his waking hours. The resulting new dataset contains a mix of indoor and outdoor scenes as well as numerous foreground objects.The creators first analysis goal is to create a visual summary of the subjects two weeks of life using unsupervised algorithms that would automatically discover recurrent scenes, familiar faces or common actions. Direct application of existing algorithms, such as panoramic stitching (e.g. Photosynth) or appearance-based clustering models (e.g. the epitome), is impractical due to either the large dataset size or the dramatic variation in the lighting conditions.The authors dubbed this type of data "All I have Seen" (AIHS, meant to be pronounced similar to "eyes"). While these types of datasets have been assembled before, it is our belief that with the proliferation of mobile devices and the availability of cloud computing, the time is now more appropriate than ever for research into this type of data acquisition, unsupervised techniques for data analysis and applications on top of them.Structural epitome: a way to summarize ones visual experienceNebojsa Jojic and Alessandro Perina and Vittorio MurinoNIPS 2010

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