There are multiple convolutional filters available for us to use in Convolutional Neural Networks (CNNs) to extract features from images. In this blog, I will explain how these different convolution operations work in depth and illustrate some design techniques for different filters. To learn more about CNNs and their drawbacks, you can read my previous article.

CNN have brought about huge changes in computer vision and other image related tasks. Although it is an old algorithm, it did not get much attention until 2012 due to lack of data and computational resources. When the first CNN (AlexNet) was used in the ImageNet competition in 2012, it improved the accuracy of prediction, by more than 15% as compared to the 2011 winner. So the convolution boom began.

Research and advancements in the CNN algorithm and architectures have turned CNN into a big hammer capable of nailing down any problem related to computer vision. They have displayed better accuracy than humans in classifying images.

But rather than talking about how CNNs work, in this article we will focus on the heart of the CNN algorithm and what makes them so powerful: the convolution operation.