Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14949
Title: Egf: An Improved Edge Detection Model for Low-Resolution Images
Authors: Deepak Raj, D M
Shanmuganathan, Harinee
Geetha, A
Keerthika, V
Keywords: Edge Detection
Gaussian Filter
Image Processing
Machine Learning
Pre-Processing
Issue Date: 2023
Publisher: 2023 2nd International Conference on Futuristic Technologies, INCOFT 2023
Institute of Electrical and Electronics Engineers Inc.
Abstract: Edge detection can benefit many different industries and domains, including computer vision, machine learning, image analysis, remote sensing, thermal imaging, pattern recognition, and medical imaging. The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in a picture serve as the object's limits and can reveal crucial details about the object's size, shape, and position. Since low-resolution images have low pixel densities or pixel values, which muddy the images, detecting edges in them is demanding work. This paper proposes a novel edge-detection approach called EGF (Extended Gaussian Filter) for low-resolution images. EGF utilizes the basic concept of Gaussian filter to find the edges of images. The objective function of EGF is developed to reduce the noise and pixel differentiation in images. The outcomes show that the suggested strategy outperforms the conventional edge detection technique. © 2023 IEEE.
URI: https://doi.org/10.1109/INCOFT60753.2023.10425353
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14949
ISBN: 9.79835E+12
Appears in Collections:Conference Papers

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