A Multiagent System for Edge Detection and Continuity Perception on Fish Otolith Images

Guillaud, Anne; Troadec, H.; Benzinou, Abdesslam; Bihan, Jean Le

doi:10.1155/s1110865702204023

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Autonomous agents are ring-like segments that automatically evolve within the images and fit on the growth structures (Guillaud et al 2002a(Guillaud et al , 2002bRodin et al 2004).…”

Section: Introductionmentioning

confidence: 99%

A new method for robust feature extraction of otolith growth marks using fingerprint recognition methods

Palmer

Álvarez

Tomás

et al. 2005

Mar. Freshwater Res.

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Individual and population age structures constitute essential knowledge for proper management of commercial fisheries. Despite the important advances made in age determination using otolith growth structures, there is still a need to improve both precision and accuracy. The problem of increasing precision in age estimations has been addressed via increasing automation in the identification of growth marks. However, approaches based on otolith size, weight, perimeter, and related measurements (including contour analysis) have moderate success in age prediction. Likewise, early attempts of image analysis have reported poor results, both in cases of 1D (greyintensity profiles) or 2D images. Recent developments in image analysis have broken this trend, and fully automatic techniques could be an alternative for routine ageing in the near future. Here, we propose a new method for 2D feature extraction that provides robust numerical descriptors of the growth structures of otoliths.Extra keyword: image-analysis.

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“…Autonomous agents are ring-like segments that automatically evolve within the images and fit on the growth structures (Guillaud et al 2002a(Guillaud et al , 2002bRodin et al 2004).…”

Section: Introductionmentioning

confidence: 99%

A new method for robust feature extraction of otolith growth marks using fingerprint recognition methods

Palmer

Álvarez

Tomás

et al. 2005

Mar. Freshwater Res.

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“…Designing intelligent agent‐based systems represents very important research directions for efficient solving of a large variety of problems. Intelligent agent‐based systems are used for many problem‐solving . There are many intelligent computing systems that are agent‐based intelligent systems based on their properties but are not called agent‐based systems.…”

Section: Problem‐solving Intelligence Of the Agent‐based Systemsmentioning

confidence: 99%

“…Intelligent agent-based systems are used for many problem-solving. 1, 3,4,7,33,34 There are many intelligent computing systems that are agent-based intelligent systems based on their properties but are not called agent-based systems. The large usage of intelligent agent-based system motivates the necessity of researches related to the designing of metrics for measuring their intelligence.…”

Section: Definitions Of Intelligent Agent-based Systemsmentioning

confidence: 99%

MetrIntPair-A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

Iantovics

Rotar

Niazi

2017

Int. J. Intell. Syst.

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In this paper, we propose a novel metric called MetrIntPair (Metric for Pairwise Intelligence Comparison of Agent‐Based Systems) for comparison of two cooperative multiagent systems problem‐solving intelligence. MetrIntPair is able to make an accurate comparison by taking into consideration the variability in intelligence in problem‐solving. The metric could treat the outlier intelligence indicators, intelligence measures that are statistically different from those others. For evaluation of the proposed metric, we realized a case study for two cooperative multiagent systems applied for solving a class of NP‐hard problems. The results of the case study proved that the small difference in the measured intelligence of the multiagent systems is the consequence of the variability. There is no statistical difference between the intelligence quotients/level of the multiagent systems. Both multiagent systems should be classified in the same intelligence class.

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“…Intelligent cooperative multiagent systems (ICMASs) are applied for a large diversity of difficult real-life problem solving tasks [1][2][3][4][5][6]. In cooperative multiagent systems (CMASs), the intelligence could be considered at the system's level.…”

Section: Introductionmentioning

confidence: 99%

MetrIntSimil—An Accurate and Robust Metric for Comparison of Similarity in Intelligence of Any Number of Cooperative Multiagent Systems

2018

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Intelligent cooperative multiagent systems are applied for solving a large range of real-life problems, including in domains like biology and healthcare. There are very few metrics able to make an effective measure of the machine intelligence quotient. The most important drawbacks of the designed metrics presented in the scientific literature consist in the limitation in universality, accuracy, and robustness. In this paper, we propose a novel universal metric called MetrIntSimil capable of making an accurate and robust symmetric comparison of the similarity in intelligence of any number of cooperative multiagent systems specialized in difficult problem solving. The universality is an important necessary property based on the large variety of designed intelligent systems. MetrIntSimil makes a comparison by taking into consideration the variability in intelligence in the problem solving of the compared cooperative multiagent systems. It allows a classification of the cooperative multiagent systems based on their similarity in intelligence. A cooperative multiagent system has variability in the problem solving intelligence, and it can manifest lower or higher intelligence in different problem solving tasks. More cooperative multiagent systems with similar intelligence can be included in the same class. For the evaluation of the proposed metric, we conducted a case study for more intelligent cooperative multiagent systems composed of simple computing agents applied for solving the Symmetric Travelling Salesman Problem (STSP) that is a class of NP-hard problems. STSP is the problem of finding the shortest Hamiltonian cycle/tour in a weighted undirected graph that does not have loops or multiple edges. The distance between two cities is the same in each opposite direction. Two classes of similar intelligence denoted IntClassA and IntClassB were identified. The experimental results show that the agent belonging to IntClassA intelligence class is less intelligent than the agents that belong to the IntClassB intelligence class.

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A Multiagent System for Edge Detection and Continuity Perception on Fish Otolith Images

Cited by 8 publications

References 12 publications

A new method for robust feature extraction of otolith growth marks using fingerprint recognition methods

A new method for robust feature extraction of otolith growth marks using fingerprint recognition methods

MetrIntPair-A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

MetrIntSimil—An Accurate and Robust Metric for Comparison of Similarity in Intelligence of Any Number of Cooperative Multiagent Systems

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