CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers

Understand

Deep Convolutional Neural Networks (CNNs) are empirically known to be invariant to moderate translation but not to rotation in image classification.

• This paper proposes a deep CNN model, called CyCNN, which exploits polar mapping of input images to convert rotation to translation.
• To deal with the cylindrical property of the polar coordinates, we replace convolution layers in conventional CNNs to cylindrical convolutional (CyConv) layers.
• A CyConv layer exploits the cylindrically sliding windows (CSW) mechanism that vertically extends the input-image receptive fields of boundary units in a convolutional layer.