Comparative analysis of spatial filtering and temporal filtering in convolutional neural networks

Abstract

Convolutional neural networks, or CNNs, have transformed machine learning, especially in the interpretation of images and videos. CNNs use spatial filtering to extract static features from images, while temporal filtering allows them to also extract dynamic data, like video sequences. Spatial and temporal filtering are compared in this research, which also examines their theoretical foundations, applications, advantages, disadvantages, and use cases. By contrasting different filtering mechanisms, we hope to shed light on their uses and help researchers choose the best filtering methods for a range of tasks.