Understanding Uncertainty Issues in the Exploration of Fish Counts

  title={Understanding Uncertainty Issues in the Exploration of Fish Counts},
  author={Emma Beauxis-Aussalet and Lynda Hardman},
Several data analysis steps are required for understanding computer vision results and drawing conclusions about the actual trends in the fish populations. Particular attention must be drawn to the potential errors that can impact the scientific validity of end-results. This chapter discusses the means for ecologists to investigate the uncertainty in computer vision results. We address a set of uncertainty factors identified by interviewing both ecology and computer vision experts, as discussed… 


Multifactorial uncertainty assessment for monitoring population abundance using computer vision
  • Emma Beauxis-Aussalet, L. Hardman
  • Environmental Science, Computer Science
    2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
  • 2015
A framework for assessing the multifactorial uncertainty propagation along the data processing pipeline is proposed, which integrates methods from both computer vision and ecology domains, and aims at supporting the statistical analysis of abundance trends for population monitoring.
A case study of trust issues in scientific video collections
The main finding is that disclosing details about the video processing and provenance data allows biologists to compare the results with their traditional statistical methods, thus increasing their trust in the results.
A video processing and data retrieval framework for fish population monitoring
This paper aims at describing the system's underlying video processing and workflow low-level details, and their connection to the user interface for on-demand data retrieval by biologists.
Statistical Analysis with Missing Data.
Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation Methods.Estimation of