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Robust Methods for the Analysis of Images and Videos for Fisheries Stock Assessment Summary of a Workshop (2015) / Chapter Skim
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6 Shape and Motion Analysis
Pages 43-49

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From page 43...
... . FISH SIZE AND MORPHOLOGY Elizabeth Clarke, NOAA Fisheries Elizabeth Clarke stated that while it is vital to obtain accurate fish size in order to obtain corresponding age and biomass measurements, measuring fish is a timeconsuming step.
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From page 44...
... Automated measuring boards exist to help with on-deck measurements of large catches. While lasers also have been used from submersibles to obtain in situ measurements, stereo imaging cameras are currently considered state-of-the-art for underwater applications.
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From page 45...
... A ROLE FOR STATISTICAL SHAPE ANALYSIS IN FISHERIES STOCK ASSESSMENT Anuj Srivastava, Florida State University Anuj Srivastava discussed his research in the detection of the types, locations, and quantity of underwater mines. He noted that there are few mines in the images he obtained; most of what is imaged is considered clutter, such as fish or underwater debris.
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From page 46...
... However, even with a large invest ment of time, the optimal parameter and model configuration settings may never be found with manual tuning. In addition, the parameters do not then generalize, and each subsequent data stream requires this same, potentially lengthy, manual tuning procedure.
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From page 47...
... In low-resolution video, humans and other objects of interest may be only a few pixels in size, which adds to the challenge. Hoogs explained that statistical shape analysis proved to be the best tool for identification and tracking, and he showed results identifying instances of a specific activity, including finding people wearing backpacks and people doing cartwheels.
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From page 48...
... Cheng explained that optical flow analysis, the traditional method of analyz ing video, tends to blur boundaries when items are in motion. It also does not adequately address occlusion.
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From page 49...
... Miller then described the application of such a system to high-throughput brain clouds. Johns Hopkins University has a database of more than 10,000 brain images, but there is no structured index associated with the images for searching them.
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Key Terms

  • machine learning
  • statistical shape
  • hui cheng
  • fisheries stock
  • geodesic positioning

  • stock assessment
  • fish size
  • geodesic coordinates
  • anuj srivastava
  • srivastava explained

  • michael miller
  • sri international
  • computational anatomy
  • elizabeth clarke
  • biological shape

  • obtain accurate
  • elastic shape
  • florida state
  • noaa fisheries
  • feature locations

  • cheng explained
  • computational linguistics
  • key feature
  • active contour
  • contour model

  • user provides
  • anthony hoogs
  • brain images
  • identify fish
  • fish length


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
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