Color Edge Detection Using the Normalization Anisotropic Gaussian Kernel and Multichannel Fusion

Wang, Dongyun; Yin, Jiawei; Tang, Chu; Ge, Binzhao

doi:10.1109/access.2020.3044341

Cited by 5 publications

(2 citation statements)

References 30 publications

(69 reference statements)

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“…Sharp photos can only be produced in this way if you want the best outcome. Images must be viewed as arrays containing samples of continuous functions of intensity in order to identify the edges in which there is a noticeable shift in intensity due to discontinuity [21]. In this article, we use a sample photograph.…”

Section: B Edge Detectionmentioning

confidence: 99%

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

Kumar,

Uma

et al. 2023

IJRITCC

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The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%.

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Section: B Edge Detectionmentioning

confidence: 99%

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

Kumar,

Uma

et al. 2023

IJRITCC

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“…Wang et al [28] proposed an edge detector based on the NAGD and MGMF methods. They decomposed each color image into six components in the RGB and HSV model and found the gradient amplitude of the image edge by the CLAHE.…”

Section: -Introductionmentioning

confidence: 99%

An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images

Agahi¹,

Rezaei²

2021

jist

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QRFODD: Quaternion Riesz fractional order directional derivative for color image edge detection

2023

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Color Edge Detection Using the Normalization Anisotropic Gaussian Kernel and Multichannel Fusion

Cited by 5 publications

References 30 publications

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images

QRFODD: Quaternion Riesz fractional order directional derivative for color image edge detection

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