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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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4 Image Processing and Detection
Pages 29-35

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From page 29...
... , and Chuck Stewart. COMPUTER VISION UNDERWATER Clay Kunz, Google Clay Kunz began by stating that platforms to study fish populations are a solved research problem: there are now a variety of platforms (such as AUVs, diver-carried rigs, towed cameras, remotely operated vehicles, and variable-ballast floats)
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From page 30...
... To overcome radiometric calibration issues, Kunz suggested choosing the best platform to obtain physical proximity to the scene, using cameras with high dy namic range, using training data, and controlling the light sources as well as pos sible. He noted that radiometric correction techniques do not necessarily translate well across all underwater environments.
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From page 31...
... While certain fish are attracted to a camera, AUV, or trawl, and other fish avoid the hardware, rockfish move slowly enough that avoidance is not a significant concern. Singh noted the value of computer-assisted systems, rather than fully automated systems, because even a partial identification would help the fisheries community to measure abundance and classify species.
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From page 32...
... Cali bration may be a concern for light-field cameras because of differences with the air/water interface, although Singh believes this is a solvable problem. UNDERWATER TELE-IMMERSION: POTENTIAL AND CHALLENGES Ruzena Bajcsy, University of California, Berkeley Ruzena Bajcsy explained that current computer vision technology allows real time, 360-degree capture of the surrounding world.
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From page 33...
... • Synchronizing virtual workspaces to enable face-to-face virtual conversations. Bajcsy explained that tele-immersion can have a number of applications, including in the geosciences, in archaeology (to capture and digitally reconstruct information across different institutions as archaeologists remove objects)
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From page 34...
... Automatic determination of the substrate will lead to automatic selection of classifiers. Stewart concluded by noting that individual animals on land can be identified in a surprising number of species, including the elephant, jaguar, rhinoceros, seal, 2    daBoost, short for adaptive boosting, is a method of machine learning in which a number of A weak, inaccurate classifiers are combined to make a more highly accurate prediction rule (Freund and Shapire, 1997)
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From page 35...
... He recognized, though, that computer vision techniques cannot be adapted to the underwater fish environment without considering a number of important, and potentially messy, technical details. He also emphasized the use of contests as powerful tools to motivate the study of well-defined problems with clear data sets.
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Key Terms

  • image processing
  • chuck stewart
  • light sources
  • hole oceanographic
  • bajcsy explained

  • clay kunz
  • stock assessment
  • ruzena bajcsy
  • woods hole
  • oceanographic institution

  • stewart explained
  • invalid assumptions
  • hanumant singh
  • specific example
  • medical applications

  • copyright woods
  • collaborative virtual
  • image analysis
  • multiple altitudes
  • current computer

  • study fish
  • fish species
  • underwater fish
  • underwater imaging
  • counting fish

  • underwater image
  • synthesized fish
  • fish attraction
  • stock assessments
  • fish populations


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