Computer Vision (CV) has undergone a remarkable evolution over the past few decades, permeating various aspects of our daily lives. While the average person may perceive it as a new and exciting innovation, the truth is that CV has been developing since the 1970s. The early foundations laid during that time have paved the way for many of the algorithms in use today. However, around a decade ago, a new technique emerged and caught the attention of the CV community: Deep learning, a form of artificial intelligence (AI) utilizing neural networks to solve complex problems.
Deep learning quickly gained recognition for its ability to solve certain CV problems with unprecedented accuracy. It particularly excelled in challenges such as object detection and classification. This marked the beginning of a distinction between classical CV, which relied on mathematical problem-solving, and deep learning-based CV. Deep learning did not render classical CV obsolete; instead, both continued to evolve, shedding light on the challenges best suited to big data and mathematical and geometric algorithms. However, it is essential to note that deep learning’s transformative power in CV is only fully unleashed when sufficient training data is available or when logical and geometrical constraints can guide the learning process autonomously.
Traditionally, classical CV involved arduous and intricate processes. Detecting objects required proficiency in techniques like sliding windows, template matching, and exhaustive search. Feature extraction and classification demanded the development of custom methodologies. Semantic segmentation, which involved labeling each pixel within an image, was a painstaking endeavor, often leading to imperfect results. In contrast, deep learning, specifically convolutional neural networks (CNNs) and region-based CNNs (R-CNNs), has revolutionized object detection, feature extraction, and semantic segmentation.
With a well-trained network, deep learning eliminates the need for explicit, handcrafted rules. Object detection becomes mundane, even under diverse circumstances and angles, thanks to massive labeled image databases. Feature extraction achieves accurate results with competent algorithms and diverse training data. CNNs excel at this task. Semantic segmentation, a once complex and manual process, is now simplified through the use of U-net architecture.
While deep learning has undoubtedly revolutionized CV, classical techniques continue to outperform newer approaches in certain challenges. Simultaneous localization and mapping (SLAM) and structure from motion (SFM) algorithms, which involve using images to understand and map physical areas, present problems where classical CV prevails. SLAM focuses on building and updating maps while tracking the agent’s position, crucial for autonomous driving and robotic applications. SFM, on the other hand, aims to reconstruct 3D objects using multiple unordered images.
Classical CV solutions triumph in these scenarios, employing advanced mathematics and geometry. Contrary to initial assumptions, both SLAM and SFM can be executed efficiently without excessive computational power. SFM specifically relies on the camera’s intrinsic properties and image features, making it a cost-effective alternative to laser scanning. Classical CV guarantees reliable and accurate representations of objects in these domains.
It is important to recognize that deep learning cannot solve all problems as effectively as classical CV. When complex math, direct observation, and scarce training data hinder deep learning’s effectiveness, classical techniques remain the go-to solution. Each approach has its strengths and limitations, and it is crucial for engineers to identify which methods are most suitable for specific challenges. Instead of wholesale replacement, a careful evaluation of the benefits and drawbacks of each technique is necessary.
As the transition from classical CV to deep learning-based CV unfolds, it opens new doors for scalability. However, it also carries an element of bittersweetness. Classical methods, while more manual, leveraged the creativity and innovation of CV engineers. The artistry involved in teasing out features, objects, edges, and key elements is irreplaceable. With classical techniques giving way to deep learning, engineers have somewhat transformed into integrators of CV tools. While this benefits the industry, it is crucial to preserve the artistic and creative elements of the role.
Looking ahead, the focus in network development is expected to shift from “learning” to “understanding.” The main emphasis will no longer be on how much the network can learn but rather on how deeply it can comprehend information. Facilitating this comprehension with minimal intervention and excessive data will be paramount. As the next decade unfolds, surprises await the CV space. Classical CV may eventually become obsolete, and deep learning might be surpassed by yet-unheard-of techniques. However, for now, these tools stand at the forefront of CV, offering the best options for specific tasks and laying the foundation for future advancements.
The evolution of CV from classical techniques to deep learning has transformed the field in unimaginable ways. While deep learning excels in object detection, feature extraction, and semantic segmentation, classical CV techniques continue to outperform in challenges like SLAM and SFM. Rather than complete replacement, a nuanced understanding of which technique suits each problem is essential. As the industry progresses, preserving the artistry and creativity of CV engineering while embracing new technologies will be a significant challenge. The future holds exciting possibilities for CV, and the journey promises to be enlightening and transformative.
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