Contour matching for a fish recognition and migration-monitoring system

Lee, Dah-Jye; Schoenberger, Robert B.; Shiozawa, Dennis K.; Xu, Xiaoqian; Zhan, Peng

doi:10.1117/12.571789

Cited by 76 publications

(52 citation statements)

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“…However, the system is capable of running other software for fish detection/recognition given that it saves the results according to our database definitions. It would be interesting to compare the fish recognition methodologies [22,31,44] with eachother. Also if new software is created, it would be interesting to see how the performance differs from the existing software.…”

Section: Discussionmentioning

confidence: 99%

“…Early work in fish recognition [42,41] is focussed on fish on conveyor belts and classifies fish based on shape and colour. Classification of fish in aquariums and tanks [44,22] is more challenging than classification of dead fish [21]. The first research in unrestricted natural environments [31] is able to classify between two different species, where Spampinato et al (2010) classified 360 images of ten different species (which is one of the largest datasets mentioned in literature).…”

Section: Diver Observations Video Recordingmentioning

confidence: 99%

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A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

Boom

Palazzo

et al. 2014

Ecological Informatics

117

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We present a research tool that supports marine ecologists' research by allowing analysis of long-term and continuous fish monitoring video content. The analysis can be used for instance to discover ecological phenomena such as changes in fish abundance and species composition over time and area. Two characteristics set our system apart from traditional ecological data collecting and processing methods. First, the continuous video recording results in enormous data volumes of monitoring data. Currently around a year of video recordings (containing over the 4 million fish observations) have been processed. Second, different from traditional manual recording and analysing the ecological data, the whole recording, analysing and presentation of results is automated in this system. On one hand, it saves the effort of manually examining every video, which is infeasible. On the other hand, no automatic video analysis method is perfect, so the user interface provides marine ecologists with multiple options to verify the data. Marine ecologists can examine the underlying videos, check results of automatic video analysis at different certainty levels computed by our system, and compare results generated by multiple versions of automatic video analysis software to verify the data in our system. This research tool enables marine ecologists for the first time to analyse long-term and continuous underwater video records.

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Section: Discussionmentioning

confidence: 99%

Section: Diver Observations Video Recordingmentioning

confidence: 99%

A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

Boom

Palazzo

et al. 2014

Ecological Informatics

117

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“…However, we can achieve this accuracy with zero-parameter Euclidean distance and get zero error with DTW. The data donors of the fish dataset tested many shape descriptors, such as Fourier descriptors, polygon approximation and line segments to achieve the best error rate of 36.0% [19], however, we get dramatically lower error rates for both Euclidean distance and DTW.…”

Section: Effectiveness Of Shape Matchingmentioning

confidence: 99%

“…These authors are interested in classifying fish. The fish shapes are reduced down to mere 40 data points because they "… found that a reduced data set of 40 points was sufficient to retain the important shape features for comparison" [19]. This dramatic data reduction did make the similarity measure more tractable, but we wondered if the assumption that it "retain(s) the important shape features" was true.…”

Section: Brute Force Rotation Alignmentmentioning

confidence: 99%

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Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures

Keogh

et al. 2008

The VLDB Journal

123

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Shape matching and indexing is important topic in its own right, and is a fundamental subroutine in most shape data mining algorithms. Given the ubiquity of shape, shape matching is an important problem with applications in domains as diverse as biometrics, industry, medicine, zoology and anthropology. The distance/similarity measure for used for shape matching must be invariant to many distortions, including scale, offset, noise, articulation, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the representation of the data or in the similarity measure used. However, rotation invariance is noted in the literature as being an especially difficult challenge. Current approaches typically try to achieve rotation invariance in the representation of the data, at the expense of discrimination ability, or in the distance measure, at the expense of efficiency. In this work, we show that we can take the slow but accurate approaches and dramatically speed them up. On real world problems our technique can take current approaches and make them four orders of magnitude faster, without false

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Robust Underwater Fish Classification Based on Data Augmentation by Adding Noises in Random Local Regions

Wei

Huang

et al. 2018

Advances in Multimedia Information Processing – PCM 2018

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Contour matching for a fish recognition and migration-monitoring system

Cited by 76 publications

References 0 publications

A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures

Robust Underwater Fish Classification Based on Data Augmentation by Adding Noises in Random Local Regions

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