Abstract
Automatic image orientation detection is an important image processing problem, although many studies have been done on this subject. In this paper, we used Convolutional Neural Networks(CNN) to detect 0 and 180 oriented images. Each image of the data set we have used contains only one object. Thanks to this, we have demonstrated that the success of the model depends on the image class selected in the training and testing.
Keywords
Convolutional neural network
Computer science
Artificial intelligence
Image (mathematics)
Pattern recognition (psychology)
Object detection
Computer vision
Set (abstract data type)
Orientation (vector space)
Image processing
Class (philosophy)
Object (grammar)
Training set
Contextual image classification
Feature detection (computer vision)
Mathematics
Subject Areas
Advanced Image and Video Retrieval Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
Image Retrieval and Classification Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
Medical Image Segmentation Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
Citations by Year
2024: 1
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