Open Access2 Detection of Grayscale Image Implementation Using Multilayer Perceptron
Author(s) -
O. N. Reni,
E. Marpanaji
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1737/1/012013
Subject(s) - artificial intelligence , grayscale , computer science , pattern recognition (psychology) , artificial neural network , similarity (geometry) , perceptron , image (mathematics) , image processing , multilayer perceptron , computer vision
The human eye can instantly recognize two patterns of color images that are almost the same quickly, but the computer cannot directly recognize any pattern in the image. The problem faced is how the computer can recognize the image pattern entered. Pattern recognition is also a technique that aims to classify previously processed images based on similarity or similarity in characteristics. In the Artificial Neural Network there are several methods that can be used to identify image patterns, one of them with the Multilayer Perceptron architecture. Multilayer Perceptron Neural Network is a type of neural network that has the ability to detect or perform analysis for problems that are sufficient or even very complex, such as in language processing problems, recognition of a pattern and processing of an image or image. The results of the study are a system that is able to recognize grayscale image patterns and is able to provide a percentage of pattern recognition in two similar and different images.