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Basics of Image Analysis

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Hyperspectral Imaging Technology in Food and Agriculture

Part of the book series: Food Engineering Series ((FSES))

Abstract

Image analysis is used as a fundamental tool for recognizing, differentiating, and quantifying diverse types of images, including grayscale and color images, multispectral images for a few discrete spectral channels or wavebands (normally less than 10), and hyperspectral images with a sequence of contiguous wavebands covering a specific spectral region (e.g., visible and near-infrared). Earlier works on image analysis were primarily confined to the computer science community, and they mainly dealt with simple images for such applications as defect detection, segmentation and classification. Nowadays, image analysis is becoming increasingly important and widespread because it can be done more conveniently, rapidly and cost effectively (Prats-Montalbán et al. 2011). Image analysis relies heavily on machine vision technology (Aguilera and Stanley 1999). The explosive growth in both hardware platforms and software frameworks has led to significant advances in the analysis of digital images.

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Author information

Authors and Affiliations

  1. U.S. Department of Agriculture, Agricultural Research Service, Sugarbeet and Bean Reseearch Unit, 524 S. Shaw Lane, Room 105A, East Lansing, MI, 48824, USA

    Fernando Mendoza & Renfu Lu

  2. Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, Room 105A, East Lansing, MI, 48824, USA

    Fernando Mendoza

Authors
  1. Fernando Mendoza
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  2. Renfu Lu
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Correspondence to Renfu Lu .

Editor information

Editors and Affiliations

  1. Agricultural Research Service, U.S. Department of Agriculture, Athens, Georgia, USA

    Bosoon Park

  2. Agricultural Research Service, U.S. Department of Agriculture, East Lansing, Michigan, USA

    Renfu Lu

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Mendoza, F., Lu, R. (2015). Basics of Image Analysis. In: Park, B., Lu, R. (eds) Hyperspectral Imaging Technology in Food and Agriculture. Food Engineering Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2836-1_2

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