  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.

Computer Vision and Pattern Recognition