Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 13 Issue 6
Real Time 2D Convolution and Max Pooling Process
Panca Mudjirahardjo
10.7753/IJCATR1306.1003
keywords : real time; 2D convolution; max pooling; feature extraction; CNN
In Convolutional Neural Network (CNN) consists of process of 2D convolution and max pooling. 2D convolution is performed to express the shape of an object in an image. Max pooling is one way to reduce the spatial dimensions of an input volume. They are together to create an object’s feature. As we know, the feature extraction is an important part in classification and detection task. A good feature can distinguish the shape of one object from another. It will increase the classification and detection accuracy. In this paper, researcher will build and observe the real time 2-D convolution and max pooling process for feature extraction.
@artical{p1362024ijcatr13061003,
Title = "Real Time 2D Convolution and Max Pooling Process ",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="6",
Pages ="18 - 23",
Year = "2024",
Authors ="Panca Mudjirahardjo"}
The paper explains the experiment and observation of 2D convolution.
The paper explains the effect of the max pooling process.
The experiment is performed in real-time conditions.
In the simulation, a performance evaluation of the algorithm is carried out.