Efficient individual identification of zebrafish using Hue/Saturation/Value color model

Al-Jubouri, Qussay; Al-Azawi, Razi J.; Al-Taee, Majid A.; Young, Iain S.

doi:10.1016/j.ejar.2018.11.006

Cited by 18 publications

(9 citation statements)

References 33 publications

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“…The study was large because 328 fish were used to test the feasibility of using the lateral skin dot pattern of Atlantic salmon for individual identification and a 6 month period of fish growth was used to study the pattern stability during fish cultivation. Previous studies had only identified a maximum of 30 fish with semi-automated methods 18 and five fish with fully automated methods 19 , and there were none using fully automated methods for long-term identification. This study is the first in which images of fish out of the water (i.e., in the tent) and underwater (i.e., in the aquarium) were used for long-term automatic identification.…”

Section: Discussionmentioning

confidence: 99%

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Computer vision based individual fish identification using skin dot pattern

Císař

Bekkozhayeva

Movchan

et al. 2021

Sci Rep

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Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.

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Section: Discussionmentioning

confidence: 99%

“…In the aquaculture sector, machine vision is usually used to identify fish species 16 . Several studies [17][18][19][20][21][22][23] , exist on the non-invasive individual identification of fish using images but only few of them use machine vision. The majority of the studies use human experts for identification based on the fish images 20,22 .…”

mentioning

confidence: 99%

Computer vision based individual fish identification using skin dot pattern

Císař

Bekkozhayeva

Movchan

et al. 2021

Sci Rep

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“…Estimating the execution time is performed by running the algorithm under test through a specific number of loop iterations. Timestamps of the start (Tstart) and end (Tend) instants of the loop are recorded, and the execution time (Texec) is then calculated as suggested in [39]:…”

Section: Coding-process Efficiencymentioning

confidence: 99%

Towards automation of dynamic-gaze video analysis taking functional upper-limb tasks as a case study

Yaman

Sobuh

Zaid

et al. 2021

Computer Methods and Programs in Biomedicine

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“…Timestamps of the start (T start ) and end (T end ) instants of the loop are initially recorded. The execution time (T exec ) is then calculated from [26]:…”

Section: Time-complexity Analysismentioning

confidence: 99%

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

et al. 2021

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The main challenge of automatic license plate recognition (ALPR) systems is that the overall performance is highly dependent upon the results of each component in the system’s pipeline. This paper proposes an improved ALPR system for the Jordanian license plates. Ceiling analysis is carried out to identify potential enhancements in each processing stage of a previously reported ALPR system. Based on the obtained ceiling analysis results, several enhancements are then suggested to improve the overall performance of the system under study. These improvements are (i) vertical-edge histogram analysis and size estimation of the candidate regions in the detection stage and (ii) de-rotation of the misaligned license plate images in the segmentation unit. These enhancements have resulted in significant improvements in the overall system performance despite a <1% increase in the execution time. The performance of the developed ALPR is assessed experimentally using a dataset of 500 images for parked and moving vehicles. The obtained results are found to be superior to those reported in equivalent systems, with a plate detection accuracy of 94.4%, character segmentation accuracy of 91.9%, and character recognition accuracy of 91.5%.

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Efficient individual identification of zebrafish using Hue/Saturation/Value color model

Cited by 18 publications

References 33 publications

Computer vision based individual fish identification using skin dot pattern

Computer vision based individual fish identification using skin dot pattern

Towards automation of dynamic-gaze video analysis taking functional upper-limb tasks as a case study

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

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