Method and image processing entity for applying a convolutional neural network to an image

A method for applying a convolutional neural network to an image, wherein the convolutional neural network comprises a convolutional kernel for convolving with images of a first image size to render feature maps and a feature kernel trained to identify a feature in the images based on the feature maps, wherein the first image size is less than a second image size of the image to which the convolutional neural network is applied, wherein the method comprises:

• processing the image while using the convolutional kernel to render a feature map, whereby a second feature map size of the feature map is greater than a first feature map size of the feature maps with which the feature kernel was trained,
• selecting the feature kernel among a plurality of feature kernels based on a position of the feature kernel relatively the feature map, which feature kernel is trained for said position, wherein the plurality of feature kernels includes a respective feature kernel that have been trained at a respective one of nine positions within the feature map, and
• repeatedly applying the feature kernel to the feature map in a stepwise manner, referring to displacement of consecutive applications of the feature kernel to the feature map, wherein the feature kernel was trained to identify the feature based on the feature maps of the first feature map size, wherein the feature kernel has the first feature map size, wherein the feature maps were obtained by convolving the convolutional kernel over images having the first image size, which causes, at least due to the convolution, the feature map to have the second feature map size, wherein the stepwise manner is represented by a step size that is greater than half of the first feature map size.