CUDA-based hybrid intuitionistic fuzzy edge detection algorithm

Yalçin, Eyüp; Badem, Hasan; Güneş, Mahit

doi:10.1109/fuzz-ieee.2015.7338008

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The use of CUDA is not limited to solving the heat equation [14][15] [11] but several works that have used CUDA to solve other problems namely the parallel implementation of the hybrid intuitionistic fuzzy edge detection algorithm [23], the routing based on the neural network thanks to the parallelism exploit [24], the calculation of the correlation function of ensembles of Pseudo-random sequences formed automatically by CUDA program [25], and optimization of tasks that require massively parallel calculations to produce an effective implementation of the integral image algorithm [26].…”

Section: B Software Impactmentioning

confidence: 99%

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Belhaous¹,

Chokri²,

Baroud³

et al. 2021

JCOMSS

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Parallelization has become a universal technique for computing an intensive scientific simulation to shorten the execution time of complex problems. It consists of bringing together the power of several thousand processors to perform complex calculations at high speed. The choice of the runtime environment to execute parallel programs significantly influences the execution time. For this reason, this article aims to materialize the impact of computing architectures on the performance of parallel implementations. To better achieve this contribution, we have implemented the heat equation executed on CUDA platform and we have compared the results with those of SkelGIS implementation from the literature. Through the results of the experiments, we demonstrated that the execution time of the CUDA implementation on graphics processing unit (GPU) is almost 100X faster for very large meshes compared to the other implementations.

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Section: B Software Impactmentioning

confidence: 99%

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Belhaous¹,

Chokri²,

Baroud³

et al. 2021

JCOMSS

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“…To eliminate this disadvantage, Otsu method is used. Therefore, HIFED algorithm is obtained [10]. These algorithms are used at both CPU-based and GPU-based applications.…”

Section: Image Processingmentioning

confidence: 99%

Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN

Güneş

Badem

2016

Journal of Sensors

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In recent years, computer vision systems have been used in almost every field of industry. In this study, image processing algorithm has been developed by using CUDA (GPU) which is 79 times faster than CPU. We had used this accelerated algorithm in destemming process of pepper. 65 percent of total national production of pepper is produced in our cities, Kahramanmaras and Gaziantep in Turkey. Firstly, hybrid intuitionistic fuzzy algorithm edge detection has been used for preprocessing of original image and Otsu method has been used for determining automatic threshold in this algorithm. Then the multilayer perceptron artificial neural network has been used for the classification of patterns in processed images. Result of ANN test for detection direction of pepper has shown high accuracy performance in CPU-based implementation and in GPU-based implementation.

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A New Approach for an Intuitionistic Fuzzy Sugeno Integral Using Morphological Gradient Edge Detector

Martínez

Melín

Castillo

2021

Advances and New Developments in Fuzzy Logic and Technology

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CUDA-based hybrid intuitionistic fuzzy edge detection algorithm

Cited by 3 publications

References 11 publications

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN

A New Approach for an Intuitionistic Fuzzy Sugeno Integral Using Morphological Gradient Edge Detector

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