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Low resolution image area classifier based on Convolutional Neural Network
Author(s)
Date Issued
May 19, 2021
Type
Conference Paper
Abstract
Deep learning techniques are widely implemented in computer vision applications. The Convolutional Neural Networks (CNN) is a deep learning class that is the most effective in categorizing the statistical characteristics of images. It is often a challenging task to classify the frequency level region in various low-resolution image. In this research, we proposed the CNN for classification of gradient profile priors by learning on several gradient characteristics such as horizontal gradient acceleration, vertical gradient acceleration, the Relational Gradient Direction and Edge Sketch Image. This technique is used multiple building blocks to designed features through backpropagation with automatic and adaptive spatial hierarchies learning. The performance comparison was improved in classification of the frequency level area in various low-resolution image input that was illustrated in the experimental results which evaluate with several predictive and conventional classification techniques.
Citation
Ecti Con 2021 2021 18th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Smart Electrical System and Technology Proceedings, 246-249, 2021