# Deep Learning vs. Traditional Computer Vision

arXiv: https://arxiv.org/abs/1910.13796 · HTML: https://mlsanity.com/paper/1910.13796/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 30 of 94)

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- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [The Big Data Myth: Using Diffusion Models for Dataset Generation to Train Deep Detection Models](https://mlsanity.com/paper/2306.09762/) (2023)
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- [gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method](https://mlsanity.com/paper/2408.14674/) (2024)
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- [What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review](https://mlsanity.com/paper/2105.11277/) (2021)
- [A Survey on Uncertainty Toolkits for Deep Learning](https://mlsanity.com/paper/2205.01040/) (2022)
- [Synthetic data generation method for data-free knowledge distillation in regression neural networks](https://mlsanity.com/paper/2301.04338/) (2023)
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- [Model-Agnostic Federated Learning](https://mlsanity.com/paper/2303.04906/) (2023)

## Abstract (arXiv)

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