Robust Methods for the Analysis of Images and Videos for Fisheries Stock Assessment Summary of a Workshop (2015) / Chapter Skim
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2 Setting the Stage
2 Setting the Stage
Pages 4-20
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TYPES OF DATA USED IN FISHERY STOCK ASSESSMENTS Allan Hicks, NOAA Fisheries Allan Hicks began by defining fishery stock assessment models as "demographic analyses designed to determine the effects of fishing on fish populations 4
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Most fish abundance data are relative and provide information about changes from previous observations. While relative abundance provides information about trends in the fisheries populations, it does not provide information about total absolute biomass, the absolute mass of a given species in a particular area or fishery.
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Fish can be tagged to see if they return to an area; one can make visual observations of habitat; and one can make environmental observations, such as sea surface temperature. Hicks explained that images and videos can assist in data collection and im prove stock assessments due to the following: • Fish mortality can be decreased with the increased use of video and images.
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Rather, it can be considered a stochastic process that evolves in time, and the sampling process is also stochastic. Thompson explained the spatial-temporal population model, which is used to assess the effectiveness of various sampling designs.
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NOAA FISHERIES STRATEGIC INITIATIVE ON AUTOMATED IMAGE ANALYSIS Benjamin Richards, NOAA Fisheries Benjamin Richards chairs the NOAA Fisheries Strategic Initiative on Auto mated Image Analysis. Another NOAA initiative has been established to examine the related topic of sampling in untrawlable habitats.
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The workshop specifically recommended increasing interdisciplinary collaboration between the marine research and computer vision communities, creating an international working group for the automated analysis of marine species, developing a database of commonly encountered fish that is accessible to the user community, and optimizing the allocation of resources and automation. Richards explained that image data sets can be broken into categories: still versus video, mono versus stereo, static versus dynamic backgrounds, and natural versus artificial lighting.
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QuadCam. QuadCam, a stereo camera platform used by the Southeast Fisheries Science Center to study reef fish, looks for fish against a compli cated coral reef background.
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(AFSC is used by the Pacific Islands Fisheries Science Center and University of Hawai'i to target deepwater bottom fish using ambient lighting at disMay!
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Several participants suggested that NOAA advertise these programs more widely in the computer vision community to bring in new participants who may not be aware of these opportunities. OVERVIEW OF COMPUTER VISION Ruzena Bajcsy, University of California, Berkeley Ruzena Bajcsy explained that she would not discuss computer vision as a whole, but instead would focus on the specific computer vision challenges posed by fish eries stock assessment.
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at the maximum sustainable yield. Bajcsy stated that tasks 2, 3, and 4 have relationships that change as a function of time, and she noted that computer vision can be used to classify different species using the outline of the fish and standard machine learning technology for classification.
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Bajcsy concluded by stating that there is a clear need for the fisheries and com puter vision communities to collaborate for mutual benefit. She suggested that the analyses of fishery data be framed as a food security issue, not just an ecological issue, to highlight its importance.
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… Motor Controllers … … Perception Behavior Biomechanical Model Motor Display Model FIGURE 2.3 Schematic diagram of the components of the artificial fish model. SOURCE: Courtesy of Professor Demetri Terzopoulos, University of California, Los Angeles.
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Terzopoulos indicated that the various fish display models were created using image-based modeling. • Body model.
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THE FISH4KNOWLEDGE PROJECT: AUTOMATED UNDERWATER VIDEO ANALYSIS FOR FISH POPULATION MONITORING Concetto Spampinato, Università di Catania (Italy) Concetto Spampinato described the Fish4Knowledge (F4K)
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Spampinato explained that all aspects of F4K remain publicly available, including the source code,6 user interface,7 and data.8 Spampinato described the video analysis system in detail and presented a schematic of the analysis system (shown in Figure 2.4)
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F4K annotated two data sets: one data set consisted of 20 million images that were labeled as either having a fish or not having a fish; a second data set consisted of 2 million images annotated with labels of the 23 most common fish species. 9 ernel K density estimation is a smoothing function that non-parametrically estimates the probability density function of a random variable.
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UNDERSEE. This project will investigate how to automatically adapt to a change in domain using semantics-guided computer vision approaches.
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