A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral

Mendoza, Olivia; Melín, Patricia; Licea, Guillermo

doi:10.1016/j.ins.2008.11.018

Cited by 78 publications

(26 citation statements)

References 24 publications

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“…[27]. Choquet Integral was used to improve face recognition together with Artificial Neural Networks [12,13]. Choquet Integral formula [28] is described as follows:…”

Section: Feature Modelling Using Choquet Integralmentioning

confidence: 99%

“…Features extraction techniques include chain code, piece-wise linear regression, and curve fitting. Multiple Linear Regression (MLR) in Choquet integral also used in face recognition improvement to enhance learning result from neural networks [12][13]. In [12] the Choquet integral was used as a mechanism to integrate information resulted from neural networks used to learn Sobel edges and morphological gradients, but still needs more testing and training samples.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Andriana¹,

Prihatmanto²,

Hidayat³

et al. 2017

ijeei

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Understanding images by recognizing its objects is still a challenging task. Tracking of moving human and recognition have been developed by researchers but not yet shows enough information needed for recognition. Initially, a tracking process of an object starts with detection and recognition of the object in a static pose and position, and then continues in movement in different poses. Available moving human recognition methods still has error in classification and need a huge amount of examples which may still be incomplete. Human face and body posture characteristics such as size of the eyes, nose, mouth, or fat or thin bodies, are important visual features in different poses for personal identification to increase accuracy of human recognition system, and it is still rare in researches. This paper attempts to describe visual features that best known for human, but hard to be recognized by machines. Curve fitting approaches to face and body posture features are also introduced to capture exact patterns of the features. Body postures are also preprocessed with a Kinect depth camera, and also compared to popular and recent methods of visual object recognition. Finally, we demonstrate our method can be useful for visual object classification. Probabilities of personal identification can be increased by using different poses and characteristics of smaller detail features through body postures and face areas. More detail features will richen comparison data samples for higher recognition accuracy.

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“…[27]. Choquet Integral was used to improve face recognition together with Artificial Neural Networks [12,13]. Choquet Integral formula [28] is described as follows:…”

Section: Feature Modelling Using Choquet Integralmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Andriana¹,

Prihatmanto²,

Hidayat³

et al. 2017

ijeei

View full text Add to dashboard Cite

show abstract

“…The Sobel operator is applied to a digital image in gray scale, is a pair of 3 9 3 convolution masks, one estimating the gradient in the x-direction (columns) (12) and the other estimating the gradient in the y-direction (rows) (13) [19]. If we have I m,n as a matrix of m rows and r columns where the original image is stored, then g x and g y are matrices having the same dimensions as I, which at each element contain the horizontal and vertical derivative approximations and are calculated by (14) and (15) [19].…”

Section: Sobelmentioning

confidence: 99%

“…If we have I m,n as a matrix of m rows and r columns where the original image is stored, then g x and g y are matrices having the same dimensions as I, which at each element contain the horizontal and vertical derivative approximations and are calculated by (14) and (15) [19]. In the Sobel method the gradient magnitude g is calculated by (16).…”

Section: Sobelmentioning

confidence: 99%

Face Recognition with Choquet Integral in Modular Neural Networks

Martínez

Melín

Mendoza

et al. 2014

Studies in Computational Intelligence

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In this chapter a new method for response integration, based on Choquet Integral is presented. A type-1 fuzzy system for edge detections based in Sobel and Morphological gradient is used, which is a pre-processing applied to the training data for better performance in the modular neural network. The Choquet integral is used how method to integrate the outputs of the modules of the modular neural networks (MNN). A database of faces was used to perform the pre-processing, the training, and the combination of information sources of the MNN.

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“…These kind of systems have become in part important of investigation, because different new techniques have emerged to help themselves [18,20,24,25]. Some of these techniques are fuzzy logic, neural networks, genetic algorithms, ant colony optimization and particle swarm optimization [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control

Sánchez

Melín

2014

Studies in Computational Intelligence

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In this chapter a new method of hierarchical genetic algorithm for fuzzy inference systems optimization is proposed. This method was used in two applications, the first was to perform the combination of responses of modular neural networks for human recognition based on face, iris, ear and voice, and the second one for fuzzy control of temperature in the shower benchmark problem. The results obtained by non-optimized type-2 fuzzy inference system can be improved using the proposed hierarchical genetic algorithm as can be verified by the simulations.

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A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral

Cited by 78 publications

References 24 publications

Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Face Recognition with Choquet Integral in Modular Neural Networks

Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control

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