Josh Hyman

Josh Hyman

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    Estimating the Spectral Reflectance of Natural Imagery Using Color Image Features
    Mark Hansen
    Eric Graham
    Deborah Estrin
    Workshop on Applications, Systems, and Algorithms for Image Sensing(2008)
    Preview abstract Relative spectral reflectance is an illumination invariant image feature that is related to many ecological phenomena that are difficult to measure, such as plant CO2 uptake. We describe a procedure to estimate the relative spectral reflectance of known subject using color image features. Through application, we show that this procedure produces accurate estimates in the presence of changing field conditions. Using this procedure, we can use imagers as sensors to measure natural phenomena that cannot be easily measured using any other available sensing modality. View details
    Imagers as sensors: Correlating plant CO2 uptake with digital visible-light imagery
    Eric Graham
    Mark Hansen
    Deborah Estrin
    Data Management for Sensor Networks(2007)
    Preview abstract There exist many natural phenomena where direct measurement is either impossible or extremely invasive. To obtain approximate measurements of these phenomena we can build prediction models based on other sensing modalities such as features extracted from data collected by an imager. These models are derived from controlled experiments performed under laboratory conditions, and can then be applied to the associated event in nature. In this paper we explore various different methods for generating such models and discuss their accuracy, robustness, and computational complexity. Given sufficiently computationally simple models, we can eventually push their computation down towards the sensor nodes themselves to reduce the amount of data required to both flow through the network and be stored in a database. The addition of these models turn in-situ imagers into powerful biological sensors, and image databases into useful records of biological activity. View details
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