Gaussian Blur through Parallel Computing

Ibrahim, Nahla M.; Elfarag, Ahmed Abou; Kadry, Rania

doi:10.5220/0010513301750179

Cited by 4 publications

(1 citation statement)

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“…The Gaussian blur is a convolution technique used as a pre-processing stage of many computer vision algorithms. Gaussian Blur reduces noise by blurring, smoothing, and eliminating noise in an image [24], [25]. CLAHE stands for Contrast Limited Adaptive Histogram Equalization, and it equalizes the image's value by reducing contrast amplification.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Rangkuti,

Hasbi Athala,

Haridhi Indallah

et al. 2023

JOIV : Int. J. Inform. Visualization

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Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

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Section: Methodsmentioning

confidence: 99%

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Rangkuti,

Hasbi Athala,

Haridhi Indallah

et al. 2023

JOIV : Int. J. Inform. Visualization

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Enhancing communication: Deep learning for Arabic sign language translation

Hassan,

Ali,

Sabri

2024

Open Engineering

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This study explores the field of sign language recognition through machine learning, focusing on the development and comparative evaluation of various algorithms designed to interpret sign language. With the prevalence of hearing impairment affecting millions globally, efficient sign language recognition systems are increasingly critical for enhancing communication for the deaf and hard-of-hearing community. We review several studies, showcasing algorithms with accuracies ranging from 63.5 to 99.6%. Building on these works, we introduce a novel algorithm that has been rigorously tested and has demonstrated a perfect accuracy of 99.7%. Our proposed algorithm utilizes a sophisticated convolutional neural network architecture that outperforms existing models. This work details the methodology of the proposed system, which includes preprocessing, feature extraction, and a multi-layered CNN approach. The remarkable performance of our algorithm sets a new benchmark in the field and suggests significant potential for real-world application in assistive technologies. We conclude by discussing the impact of these findings and propose directions for future research to further improve the accessibility and effectiveness of sign language recognition systems.

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The Improvement of 1D Gaussian Blur Filter using AVX and OpenMP

2022

2022 22nd International Conference on Control, Automation and Systems (ICCAS)

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Gaussian Blur through Parallel Computing

Cited by 4 publications

References 0 publications

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Enhancing communication: Deep learning for Arabic sign language translation

The Improvement of 1D Gaussian Blur Filter using AVX and OpenMP

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