#054 - Evolution of Computer Vision: From Classical Algorithms to Deep Learning Networks, AMD Acquires Nod.ai to Boost its AI Software Ecosystem, Foxconn & Nvidia Partner to Accelerate EV Development

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Oct 18, 2023

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AI BYTE # 1 📢 : The Evolution of Computer Vision: From Classical Algorithms to Deep Learning Networks

⭐ Computer vision (CV) is a fascinating field that has been evolving for decades, from the early studies in the 1970s to the recent breakthroughs in deep learning.

Deep Learning has transformed many CV problems, such as object detection, feature extraction, and semantic segmentation. However, Deep Learning is not a silver bullet that can solve every CV challenge.

In fact, there are still some tasks that are better suited for classical CV algorithms, such as Simultaneous Localization and Mapping (SLAM) and Structure From Motion (SFM).

In this post, I will share some insights on how Deep Learning and classical CV coexist and complement each other, and why we should not discard the old methods in favor of the new ones.

Deep learning is a form of AI that uses neural networks to learn from data and solve complex problems. It has shown remarkable results in many CV tasks, especially when paired with large labeled image databases.

For example, Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs) have made object detection much easier and more accurate than before, without requiring explicit rules or sliding windows.

Similarly, CNNs and U-net architectures have simplified feature extraction and semantic segmentation, eliminating the need for handcrafted methodologies or region separation.

However, Deep Learning is not a magic solution that can handle every CV problem. It has some limitations and drawbacks, such as requiring a lot of data and computational power, being prone to overfitting or underfitting, and lacking interpretability or explainability.

Moreover, Deep Learning is not very effective when it comes to problems that involve complex mathematics or geometry, such as SLAM and SFM.

SLAM is a technique that allows an agent (such as a robot or a car) to build and update a map of an environment while keeping track of its location within the map. This is essential for autonomous navigation and exploration.

SFM is a technique that allows us to create a 3D reconstruction of an object or a scene using multiple images taken from different viewpoints. This is useful for applications such as 3D modeling, virtual reality, or augmented reality.

Both SLAM and SFM rely on classical CV algorithms that use advanced mathematics and geometry to estimate the camera pose, the 3D structure, and the motion of the scene.

These algorithms are based on close approximations that make the computational requirements more manageable. They also use only the camera’s intrinsic properties and the features of the image, which makes them more cost-effective than other methods such as laser scanning.

These classical CV algorithms have proven to be reliable and accurate in solving SLAM and SFM problems, while Deep Learning approaches have not been able to match their performance or efficiency. Therefore, classical CV still dominates these specific challenges.

The lesson here is that we should not blindly replace classical CV with deep learning, but rather identify which problems are best solved by which techniques.

We should also appreciate the artistry and creativity involved in classical CV methods, which require us to formulate and solve mathematical problems rather than rely on data-driven learning.

I believe that the future of CV will not be about learning alone, but also about understanding. We should aim to develop networks that can comprehend information deeply and reach meaningful conclusions with minimal intervention.

We should also seek to integrate classical CV algorithms with Deep Learning networks to leverage their strengths and overcome their weaknesses.

I hope you enjoyed this post and learned something new about CV. If you have any questions or comments, please feel free to share them below. Thank you for reading!

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