Deep Learning vs. Traditional Computer Vision

TL;DR

This paper compares deep learning and traditional computer vision, discussing their benefits, drawbacks, and potential hybrid approaches to enhance performance in emerging domains like panoramic and 3D vision.

Contribution

It provides an analysis of both approaches and reviews recent hybrid methodologies that combine classical techniques with deep learning.

Findings

Hybrid methods improve performance in specific applications.

Traditional techniques remain relevant alongside deep learning.

Combining approaches addresses limitations of each method.

Abstract

Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised