An automatic counting system for transparent pelagic fish eggs based on computer vision

Duan, Yane; Stien, Lars Helge; Thorsén, Anders; Karlsen, Ørjan; Sandlund, Nina; Li, Daoliang; Fu, Zetian; Meier, Sonnich

doi:10.1016/j.aquaeng.2015.05.001

Cited by 36 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dead eggs were removed daily from the bottom of the exposure and recovery tanks and counted from digital photos using tools from MATLAB R2012b (The MathWorks, Inc., Natick, MA) (see cumulative egg mortality in Figure S2). The execution pipeline for the computer vision-based automatic counting is described in detail in Duan et al…”

Section: Methodsmentioning

confidence: 99%

Effects of Exposure to Low Concentrations of Oil on the Expression of Cytochrome P4501a and Routine Swimming Speed of Atlantic Haddock (Melanogrammus aeglefinus) Larvae In Situ

Cresci

Paris²,

Browman

et al. 2020

Environ. Sci. Technol.

View full text Add to dashboard Cite

Exposure to environmentally relevant concentrations of oil could impact survival of fish larvae in situ through subtle effects on larval behavior. During the larval period, Atlantic haddock (Melanogrammus aeglefinus) are transported toward nursery grounds by ocean currents and active swimming, which can modify their drift route. Haddock larvae are sensitive to dispersed oil; however, whether exposure to oil during development impacts the ability of haddock larvae to swim in situ is unknown. Here, we exposed Atlantic haddock embryos to 10 and 80 μg oil/L (0.1 and 0.8 μg ∑PAH/L) of crude oil for 8 days and used a novel approach to measure its effect on the larval swimming behavior in situ. We assessed the swimming behavior of 138 haddock larvae in situ, in the North Sea, using a transparent drifting chamber. Expression of cytochrome P4501a (cyp1a) was also measured. Exposure to 10 and 80 μg oil/L significantly reduced the average in situ routine swimming speed by 30–40% compared to the controls. Expression of cyp1a was significantly higher in both exposed groups. This study reports key information for improving oil spill risk assessment models and presents a novel approach to study sublethal effects of pollutants on fish larvae in situ.

show abstract

Section: Methodsmentioning

confidence: 99%

Effects of Exposure to Low Concentrations of Oil on the Expression of Cytochrome P4501a and Routine Swimming Speed of Atlantic Haddock (Melanogrammus aeglefinus) Larvae In Situ

Cresci

Paris²,

Browman

et al. 2020

Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In the future, we will investigate to release this QP problem to a linear problem, by using the expectation-maximization (EM) framework to release the hinge loss to a linear function. Moreover, we also plan to extend the proposed algorithm to different applications, e.g., bioinformatics [43,34,36,47], computer vision [32,37,6,31], and information retrieval [42,16,11,33].…”

Section: Discussionmentioning

confidence: 99%

Supervised learning of sparse context reconstruction coefficients for data representation and classification

Liu

Wang

Yin

et al. 2015

Neural Comput & Applic

View full text Add to dashboard Cite

Context of data points, which is usually defined as the other data points in a data set, has been found to paly important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context, and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.

show abstract

“…Duan et al [8] developed a computer vision-based method to realize the counting of eggs in dead fish. This was based on the principle that dead eggs float.…”

Section: Related Workmentioning

confidence: 99%

An Investigation of Parameter Optimization in Fingerling Counting Problems

Oliveira¹,

Pache²,

Rezende

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

View full text Add to dashboard Cite

The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.

show abstract

An automatic counting system for transparent pelagic fish eggs based on computer vision

Cited by 36 publications

References 13 publications

Effects of Exposure to Low Concentrations of Oil on the Expression of Cytochrome P4501a and Routine Swimming Speed of Atlantic Haddock (Melanogrammus aeglefinus) Larvae In Situ

Effects of Exposure to Low Concentrations of Oil on the Expression of Cytochrome P4501a and Routine Swimming Speed of Atlantic Haddock (Melanogrammus aeglefinus) Larvae In Situ

Supervised learning of sparse context reconstruction coefficients for data representation and classification

An Investigation of Parameter Optimization in Fingerling Counting Problems

Contact Info

Product

Resources

About