Abstract
Computer vision technology has been considered in marine ecology
research as a innovative, promising data collection method. It contrasts with traditional
practices in the information that is collected, and its inherent errors and biases.
Ecology research is based on the analysis of biological characteristics (e.g., species,
size, age, distribution, density, behaviors), while computer vision focuses on visual
characteristics that are not necessarily related to biological concepts (e.g., contours,
contrasts, color histograms, background model). It is challenging for ecologists to
assess the scientific validity of surveys performed on the basis of image analysis.
User information needs may not be fully addressed by image features, or may not
be reliable enough. We gathered user requirements for supporting ecology research
based on computer vision technologies, and identified those we can address within
the Fish4Knowledge project. We particularly investigated the uncertainty inherent
to computer vision technology, and the means to support users in considering uncertainty
when interpreting information on fish populations. We introduce potential
biases and uncertainty factors that can impact the scientific validity of interpretations
drawn from computer vision results. We conclude by introducing potential
approaches for providing users with evaluations of the uncertainties introduced at
each information processing step.
research as a innovative, promising data collection method. It contrasts with traditional
practices in the information that is collected, and its inherent errors and biases.
Ecology research is based on the analysis of biological characteristics (e.g., species,
size, age, distribution, density, behaviors), while computer vision focuses on visual
characteristics that are not necessarily related to biological concepts (e.g., contours,
contrasts, color histograms, background model). It is challenging for ecologists to
assess the scientific validity of surveys performed on the basis of image analysis.
User information needs may not be fully addressed by image features, or may not
be reliable enough. We gathered user requirements for supporting ecology research
based on computer vision technologies, and identified those we can address within
the Fish4Knowledge project. We particularly investigated the uncertainty inherent
to computer vision technology, and the means to support users in considering uncertainty
when interpreting information on fish populations. We introduce potential
biases and uncertainty factors that can impact the scientific validity of interpretations
drawn from computer vision results. We conclude by introducing potential
approaches for providing users with evaluations of the uncertainties introduced at
each information processing step.
| Original language | English |
|---|---|
| Title of host publication | Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data |
| Publisher | Springer |
| Number of pages | 12 |
| Publication status | Published - 2016 |