Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing

Melín, Patricia; Castillo, Oscar

doi:10.1007/b97585

Cited by 127 publications

(37 citation statements)

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“…Possessing crisp membership functions, T1 FSs are not able to model these uncertainties directly. T2 FSs, on the other hand, show more prosperous performance as they provide adequately satisfactory results in capturing uncertainties [41]. Because GAs are well known and have been widely used as a powerful global search technique, a large number of publications explored the use of GAs for designing fuzzy systems, so-called GFSs [42].…”

Section: Introductionmentioning

confidence: 99%

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

et al. 2015

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Abstract:The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy OPEN ACCESS Remote Sens. 2015, 7 8272 framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.

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Section: Introductionmentioning

confidence: 99%

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

et al. 2015

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“…Related works have been done in the medicine's domain (determination of some possible health conditions [1]), in biometrics domain (identification and recognition of a person [2]) and others. Biometrics plays an important role in public safety and to accurately identify each individual to distinguish them from each other [3]. The traditional methods normally make use of magnetic cards or cards with some bar codes printed on it.…”

Section: Introductionmentioning

confidence: 99%

“…The iris texture provides many minute characteristics such as freckles, coronas, stripes, furrows, crypts, etc [2]. From the birth of a person until death, the patterns of the iris are relatively constant over a person's lifetime [1,3]. Because of this uniqueness and stability, iris recognition is a reliable human identification technique.…”

Section: Introductionmentioning

confidence: 99%

IRIS Recognition System using Neural Network and Genetic Algorithm

SaishanmugaRaja¹,

Rajagopalan²

2013

IJCA

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In this paper, the author proposes a method for personal identification based on iris recognition using Genetic algorithm and Neural Network. The process of iris recognition consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. A Neural Network is used to reduce the low recognition rate, low accuracy and increased time of recovery. Here the genetic algorithm is used to optimize the Neural Networks parameters. The simulation results show a good identification rate and reduced training time.

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“…Biometrics plays an important role in public safety and to accurately identify each individual to distinguish them from each other [5]. This problem has been studied more thoroughly in recent years thanks to advances in computational power that have allowed the implementation of more complex algorithms using different techniques [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…There are several implementations of the important is by nature of using neuron to evaluate the performance gating network is based on a networks of experts [5]. In Figure Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

et al. 2012

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Abstrak Kata kunci: algoritma genetik, biometrik iris, fuzi, jaringan syaraf tiruan, optimasi Abstract This paper describes the application of modular neural network architectures for person recognition using the human iris image as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with

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Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing

Cited by 127 publications

References 0 publications

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

IRIS Recognition System using Neural Network and Genetic Algorithm

Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

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