Vision-based Automatic System for Non-contact Measurement of Morphometric Characteristics of Flatfish

Jeong, Seong‐Jae; Yang, Yaguang; Lee, Kyounghoon; Kang, Jun-Gu; Lee, Dong-Gil

doi:10.5370/jeet.2013.8.5.1194

Cited by 27 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) the weights of the sea cucumbers in air and in distilled water, and the water density of the distilled water. The water density in this study was considered as 1, because the density of pure water is 0.997 g/cm 3 at room temperature (25 °C) [10]. Fig.…”

Section: Measurement Of Actual Volume and Weightmentioning

confidence: 99%

“…To improve these methods, previous studies measured the lengths [2][3][4][5] and areas [6][7] of fish in two-dimensional images generated by a computer vision system; fish selection and weight estimation were then conducted. Lee et al (2001) performed three-dimensional measurements and restructured a two-dimensional cross-section photography scan into a three-dimensional surface using charge coupled device (CCD) cameras and a multi-line laser [8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee¹,

Kim²,

Park³

et al. 2014

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-Growth analysis and selection of sea cucumbers (Stichopus japonicas) is typically performed through length or weight measurements. However, because sea cucumbers continuously change shape depending on the external environment, weight measurement has been the preferred approach. Weight measurements require extensive time and labor, moreover it is often difficult to accurately weigh sea cucumbers because of their wet surface. The present study measured sea cucumber features, including the body length, width, and thickness, by using a vision system and regression analysis to generate R 2 values that were used to develop a weight estimation algorithm. The R 2 value between the actual volume and weight of the sea cucumbers was 0.999, which was relatively high. Evaluation of the performance of this algorithm using cross-validation showed that the root mean square error and worst-case prediction error were 1.434 g and ±5.879 g, respectively. In addition, the present study confirmed that the proposed weight estimation algorithm and single slide rail device for weight measurement can measure weights at approximately 4,500 sea cucumbers per hour.

show abstract

Section: Measurement Of Actual Volume and Weightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee¹,

Kim²,

Park³

et al. 2014

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…This correction (henceforth depth corrected TL) can be interpreted as having adjusted the ALPP to be the same as if the foreground fiducial marker was positioned at the distal plane. To calculate depth corrected TL, the width of the fish is required, which can be estimated from the length, provided the length-width relationship is known (Jeong, Yang, Lee, Kang, & Lee, 2013;Loy, Boglione, Gagliardi, Ferrucci, & Cataudella, 2000;Rohlf & Marcus, 1993;Tulli, Balenovic, Messina, & Tibaldi, 2009). However, depth corrected TL is subject to systematic error because the estimated length used to derive width is itself an underestimate.…”

Section: Correcting For Subject Profilementioning

confidence: 99%

Accurate estimation of fish length in single camera photogrammetry with a fiducial marker

Monkman

Hyder

Kaiser

et al. 2019

ICES Journal of Marine Science

View full text Add to dashboard Cite

Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems used to collect such data are varied and often complex. In contrast, digital cameras and smartphones are ubiquitous, convenient for the user and an image automatically captures much of the data normally recorded on paper as metadata. The limitations of such an approach are primarily attributed to errors introduced through both the image acquisition process and lens distortion of the collected images. In the present study, a methodology is presented to achieve accurate 2-dimensional (2-D) total length (TL) estimates of fish without specialist camera equipment or proprietary software. Photographs of depressed (flat) and fusiform fish were captured with an action camera using a background fiducial marker, positioned at the distal plane of the subject; a foreground fiducial marker, at the proximal plane of the subject and a laser marker, projected on to the subject's surface. To correct image distortions, the geometric properties of the lens were modelled with OpenCV. The accuracy of TL estimates were corrected for parallax effects using a novel iterative algorithm requiring only the initial length estimate and known morphometric relationships. OpenCV was effective in correcting image distortion, decreasing RMSE by 96% and the percentage mean bias error (%MBE) by 50%. By undistorting the image and correcting for parallax effects a % MBE [95% CIs] of-0.6% [-1.0,-0.3] was achieved and RMSE was reduced by 86% to 2.1%. Estimation of the lens to subject distance using the similar triangles calibration method resulted in the best estimation of TL. The present study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for expensive, complex or bulky camera equipment making it particularly suitable for deployment in citizen science and other volunteer based data collection endeavours.

show abstract

“…With regard to length, most of these methods measure the link length between the fish width midpoints (Strachan ; Ibrahim and Wang, ; Jeong and others ). With regard to weight estimation, the methods use a regression formula between the projected two‐dimensional (2D) view area and the weight (Gümüş and Balaban, ; Balaban and others , ).…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimum Computer Vision‐Based Automatic Abalone (Haliotis discus hannai) Grading Algorithm

Lee

Kim

et al. 2015

Journal of Food Science

Self Cite

View full text Add to dashboard Cite

An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively.

show abstract

Vision-based Automatic System for Non-contact Measurement of Morphometric Characteristics of Flatfish

Cited by 27 publications

References 17 publications

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Accurate estimation of fish length in single camera photogrammetry with a fiducial marker

Design of an Optimum Computer Vision‐Based Automatic Abalone (Haliotis discus hannai) Grading Algorithm

Contact Info

Product

Resources

About