Computer Vision (CV) has come a long way in its evolution, permeating various aspects of our daily lives. While it may seem like a recent innovation to the average person, the reality is that CV has been evolving for decades. In fact, studies conducted in the 1970s laid the early foundations for many of the algorithms that are still in use today. However, it was only about 10 years ago that deep learning, a form of artificial intelligence (AI) that utilizes neural networks, emerged as a new and promising technique for addressing complex problems. Deep learning revolutionized the field of CV, particularly in areas such as object detection and classification. This gave rise to a distinction between classical CV, which relied on mathematical problem-solving, and deep learning-based CV.
Deep learning has proven to be a game-changer in CV, especially when coupled with large labeled image databases like those of Google and Amazon. Convolutional neural networks (CNNs) and region-based CNNs (R-CNNs) have made object detection a fairly mundane task, capable of handling diverse circumstances and angles without the need for explicit rules. Feature extraction, too, has been greatly simplified with deep learning algorithms and diverse training data, allowing for high accuracy and preventing overfitting. CNNs have excelled in this area. Even semantic segmentation has seen exceptional performance with the U-net architecture, eliminating the complexity of manual processes.
Despite its remarkable capabilities, deep learning is not a one-size-fits-all solution for every CV challenge. When it comes to simultaneous localization and mapping (SLAM) and structure from motion (SFM) algorithms, classical CV techniques still outperform deep learning-based approaches. SLAM deals with building and updating maps of physical areas while keeping track of an agent’s position within the map, enabling autonomous driving and robotic vacuums. SFM, on the other hand, focuses on creating 3D reconstructions from a set of unordered images. These tasks heavily rely on advanced mathematics and geometry. Classical CV has found ways to manage the computational requirements for SLAM and has proven to be a cost-effective alternative to laser scanning in SFM.
Deep learning may be a powerful tool, but there are still challenges that classical CV techniques are better suited for. When complex math and direct observation are involved, and obtaining an adequate training dataset is difficult, deep learning becomes too powerful and unwieldy to provide elegant solutions. There is an inherent need to continue using traditional techniques for these specific problems. It’s essential to recognize that a complete replacement of classical CV with deep learning is not always the best approach. Instead, a careful evaluation of each problem is necessary to determine which technique will yield the most effective results.
As the field of CV undergoes a partial transition from classical methods to deep learning, it’s crucial to exercise caution and pay attention to the specific needs of each problem. While the scalability offered by deep learning is undeniable, there is a bittersweet element to this transition. The classical methods required a combination of both artistry and scientific understanding. Extracting features and identifying objects required deep thinking rather than just deep learning. As we move away from these creative elements, engineers are faced with the role of integrating CV tools rather than being active contributors to the creative process. Incorporating artistry into other aspects of CV will be a challenge moving forward.
Looking ahead, the development of CV networks will shift its focus from learning to understanding. The emphasis will no longer solely be on how much the network can learn, but rather on how deeply it can comprehend information. The goal is to enable the network to reach meaningful conclusions with minimal intervention, going beyond surface-level learning. The next decade is bound to bring surprises and advancements in the field of CV. Classical CV may eventually become obsolete, or an entirely new technique may emerge to surpass deep learning. However, for now, classical techniques and deep learning will continue to play vital roles in addressing specific tasks and shaping the future of CV.
The evolution of CV has been driven by a combination of classical methods and deep learning. While deep learning has revolutionized certain aspects of CV, it is essential to recognize the strengths of classical techniques in addressing specific challenges. The transition from classical to deep learning-based approaches requires careful evaluation and an understanding that both methods have their place in the field. With future advancements, the main focus of CV will shift towards understanding rather than just learning, enabling networks to reach deeper insights with minimal intervention. As the journey of CV continues, it is crucial to embrace the creativity and innovation that both classical and deep learning techniques bring to the field.
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