@inproceedings{d4fd0e14eca74aa08315cb542512c827,
title = "ContourCNN: Convolutional neural network for contour data classification",
abstract = "This paper proposes a novel Convolutional Neural Network model for contour data analysis (ContourCNN) and shape classification. A contour is a circular sequence of points representing a closed shape. For handling the cyclical property of the contour representation, we employ circular convolution layers. Contours are often represented sparsely. To address information sparsity, we introduce priority pooling layers that select features based on their magnitudes. Priority pooling layers pool features with low magnitudes while leaving the rest unchanged. We evaluated the proposed model using letters and digits shapes extracted from the EMNIST dataset and obtained a high classification accuracy.",
keywords = "CNN, Circular data, Classification, Contour, Convolutional neural netwrok, Priority pool",
author = "Ahmad Droby and Jihad El-Sana",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021 ; Conference date: 07-10-2021 Through 08-10-2021",
year = "2021",
month = oct,
day = "7",
doi = "10.1109/ICECCME52200.2021.9591095",
language = "English",
series = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021",
address = "United States",
}