Measurement of sole activity by digital image analysis

Duarte, Silvana; Reig, L.; Oca, Joan

doi:10.1016/j.aquaeng.2009.06.001

Cited by 47 publications

(36 citation statements)

References 35 publications

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“…The digital image has been widely used in several applications mainly in chemistry (Lyra et al 2009;Maleki et al 2004;Duarte et al 2009;Lapresta-Fernández et al 2009;Godinho et al 2010) and in food quality and safety control (Torrence et al 2004;Borah and Bhuyan 2005;Acevedo et al 2009;Medina et al 2010). Alimelli et al (2007), for instance, has proposed a different method for detection and quantification of total anthocyanins and polyphenols in red wine.…”

Section: Introductionmentioning

confidence: 99%

Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk

2011

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This study presents a proposition of a low-cost screening method for detection and quantification of adulterations in liquid cow's milk samples. The studied adulterations were made with water and NaOH. Digital images from the adulterated samples were obtained using a flatbed scanner, and the means of ten color parameters were used to evaluate the information from images: red, green, blue, hue, saturation, value, relative colors (r, g, and b), and intensity. Regression models for water quantification were proposed using multiple linear regression (MLR), principal components regression (PCR), and partial least squares (PLS). The best models were obtained using PCR and PLS, with root mean square error for prediction smaller than 7%. These results were compared with near-infrared (NIR), and the prediction capability was very similar. In the case of adulterations with NaOH, the colors B, S, g, and b presented the highest differences between fresh and adulterated milk samples.

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Section: Introductionmentioning

confidence: 99%

Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk

2011

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“…Examples include fish counting, size measurement, species identification, and behavioral analysis [1,7,9,12,13,18,24,20,27,25,26].…”

Section: Introductionmentioning

confidence: 99%

“…Request permissions from permissions@acm.org. the species of interest are usually pelagic [7]. Pelagic fish are mostly always in motion, thus the behavioral analysis of such fish usually consists of tracking the fish as whole objects and analyzing their swim trajectories.…”

Section: Introductionmentioning

confidence: 99%

Automated Analysis of Wild Fish Behavior in a Natural Habitat

Wang

Cullis-Suzuki

Albu

2015

Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval

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This paper proposes a novel approach for the analysis of movement and behavior of the Plainfin midshipman (Porichthys notatus) in the wild. It is based on underwater video recordings of the fish in their natural habitat taken inside their nests during reproductive months. During this time, alpha male Plainfin midshipmen rarely leave their nests as they are guarding their eggs, so the proposed approach addresses the issue of detecting subtle motion and nesting behavior as the fish remains relatively sedentary. To the best of our knowledge, this is the first paper to propose an automated method to analyze subtle movements of a highly territorial animal in its natural habitat.Motion detection uses the displacement of SURF (Interest point algorithm) key-point movements from frame to frame to analyze the amount of movement by the fish. Kmeans clustering and other outlier removal techniques are then used to differentiate fish motion from small moving objects in the background and foreground. The analysis of fish behavior uses similarity-based periodicity detection combined with the K-neighbors classifier. Experimental validation with respect to expert-annotated ground truth shows excellent performance for both motion and behavior detection approaches.

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“…Duarte et al (2009) identified the activity of flat fish 'taking off' and 'surface swimming' as a preliminary measure of their welfare. Branson and Belongie (2005) also developed a vision system to give an indication of the animal welfare by monitoring the behaviours of multiple mice from a side view.…”

Section: Research Based Vision Systems That Determine Behaviourmentioning

confidence: 99%

A brief review of the application of machine vision in livestock behaviour analysis

Tscharke

Banhazi²

2016

JAI

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It is desirable to increase the frequency between livestock welfare assessments to enhance problem identification and consumer confidence in livestock welfare management. However, animal welfare is difficult to monitor in practice, due to the inefficiencies involved in manually documenting and determining, animal behaviour, social interaction and health condition of large numbers of animals. Furthermore, the effectiveness of a welfare assessment relies on the intuition of the observer which may vary considerably between assessors. Hence, this review investigates the application of machine vision systems to recognise and monitor the behaviour of animals in a quantitative manner. Behaviour-recognition concepts, techniques, and current behaviour monitoring systems are reviewed. Findings indicate that further research is required to develop systems that can monitor the behaviour and welfare of animals' more efficiently and effectively in commercially realistic environments. Introduction: Livestock Behaviour and Vision SystemsMeasuring and assessing the behaviour of livestock is important as it can be used to indicate their welfare status (Dutta et al., 2015;Porto et al., 2014). Behaviour is formed from an animal's continuous interaction with its environment (Figure 1). Animal behaviour is a response to an internal stimuli (physiological) such as hunger or an external stimuli such as climate. If the goal of the behaviour is not, or cannot, be achieved the animal may change its behaviour or physiological response (Busse et al., 2015). There is potential for welfare problems to arise when there are inadequate environmental enrichments to support the behavioural needs of livestock.It has been argued that the husbandry methods used in intensive livestock production have resulted in the deprivation of some naturally occurring behaviours (Fraser, 1983). Producers have a strong interest in maintaining the welfare of their livestock from both economic and ethical perspectives. As often proactive welfare management has a positive effect on a farm's production efficiency and the quality and marketability of the end product (Kashiha et al., 2013).The observation processes currently used to measure livestock behaviour are subjective, as farm workers perform the welfare assessment. The worker's involvement in these tasks are necessary and therefore increase the demand on the labour and costs associated with monitoring animal behaviour. These factors have potential to influence the level of attention each animal receives (Oczak et al., 2013;Pereira et al., 2013). Thus, behavioural measurements are open for interpretation and have potential to be overlooked.Given the problems mentioned above, this review investigates the application of machine vision systems to recognise and monitor the behaviour of animals in a quantitative manner. Specifically in this article, behaviour-recognition concepts, techniques, and current behaviour monitoring systems are reviewed. Applying machine vision systems to the behaviour recognition taskTo over...

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Measurement of sole activity by digital image analysis

Cited by 47 publications

References 35 publications

Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk

Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk

Automated Analysis of Wild Fish Behavior in a Natural Habitat

A brief review of the application of machine vision in livestock behaviour analysis

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