Deep Learning vs. Traditional Computer Vision

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.

Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection

“…The difficulty with these traditional approaches is the necessity to choose which features are important for each task. As the number of organisms to classify increases, feature extraction becomes more complex [22].…”

Data‐driven nonrigid object feature analysis: Subspace application of incidence structure

“…However, methods to automatically learn good features in an integrated fashion into deep network layers is of current debate and research. Furthermore, presently there remain applications where traditional computer vision models are still necessary to build upon recent progresses in deep learning object detection, such as 3D object recognition, moving object detection, and scene understanding. Based on the current capabilities of deep learning object recognition, we suggest, through further experimentation, that the data‐driven inference routine can be represented as a series of processing layers as part of a standard deep learning model.…”

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

“…However, deep learning is well known to operate similar to a black box. It requires a massive amount of data to fully support its rich parameterization and provides explainable predictions seem to be a substantially challenging task [33], [40]. Meanwhile, topic modeling can provide the word co-occurrence relation to supplement for information loss.…”