Computer Vision
computer vision is a field of artificial intelligence (AI) that enables machines to understand and interpret the visual world, just as humans do.
From identifying objects in an image to the detailed analysis of real-time videos, computer vision is transforming industries and revolutionizing production processes in sectors such as manufacturing, healthcare, transportation, and energy.
In this article, we will explore in depth the past, present, and future of this field, covering classical strategies, the revolution brought by deep learning, and the most recent advances with transformers and large vision models.
Furthermore, we will discuss the challenges of image acquisition using different types of cameras and specialized sensors, such as hyperspectral and LIDAR.
We will also address the use of drones, synthetic data, and generative AI, showing how these tools are applied in critical sectors, such as oil and electric power, with a highlight on the innovative solutions from Pixforce.ai.
What is Computer Vision?
Artificial Intelligence is a field of computer science that seeks to create systems capable of performing tasks that normally require human intelligence, such as speech recognition, decision making, language translation, and image interpretation.
In a practical way, this technology is capable of capturing images, classifying them, and grouping them according to a stipulated pattern. Check it out below:
[caption id=”attachment_2461” align=”alignnone” width=”640”] Didactic example of how Computer Vision can capture, classify, and group[/caption] AI is broad and encompasses several subareas, such as machine learning and deep learning, which are specific to teaching machines to learn from data.
To make it easier, let's give an example. Think of a voice assistant on your phone, like Siri or Google Assistant. When you ask a question or give a command, it understands and responds using AI to interpret your speech and perform the corresponding action.
Definition and Basic Concepts
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Machine Learning (ML)
Machine Learning is a subarea of AI that involves using algorithms to teach computers to learn from data and make predictions or decisions without being explicitly programmed for each task.
Remember Netflix's movie recommendation system. It learns from the history of the movies you watched and evaluates what your preferences are, suggesting other movies and series you will probably like.
Deep Learning
Deep Learning is a more advanced technique within machine learning that uses deep neural networks to analyze complex patterns in data. Convolutional neural networks (CNNs) are a common example of deep learning and are widely used in computer vision to recognize objects in images.
An easy-to-understand example is security systems that use cameras to detect people or vehicles. They use deep learning to analyze images and recognize whether an object is a car, a person, or something else.
Generative AI
Generative AI refers to algorithms that not only analyze data but also generate new data that looks authentic.
Examples include GANs (Generative Adversarial Networks), which are used to create images, music, or text that appear to have been made by humans.
Imagine an application that generates fictitious human faces for games or social networks. These faces are created by generative AI that has learned from thousands of real faces to produce new images that look authentic.
The Past of Computer Vision
The history of computer vision begins in the 1960s and 1970s, when the first algorithms were developed to interpret digital images.
Initially, computer vision was limited to simple tasks, such as edge detection and basic shape recognition. Algorithms like the Canny operator for edge detection and the Hough transform for geometric shape detection were fundamental in this early stage.
These techniques allowed computers to identify outlines and patterns in images, but they were still far from providing the complex visual understanding we see today.
In 1972, the Texas Instruments company created the world's first digital camera. Three years later, in 1975, the Cromemco Cyclops became the first digital camera on the market capable of connecting to a computer. From there, and with the creation of the first sensors, it became possible to interpret images. See in the image below:
[caption id=”attachment_2463” align=”alignnone” width=”640”] Image of the 1st Digital Camera - 1975 | Cromemco Cyclops[/caption] In the 1990s, there were significant advances with the development of algorithms based on local features, such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features).
These methods allowed the matching of points in different images, enabling the construction of three-dimensional maps from multiple views and the detection of objects in complex scenes.
However, this phase of computer vision was still limited by the algorithms' ability to recognize patterns only in specific and controlled contexts.
The need to manually label images and adjust parameters for different scenarios made the process laborious and unscalable.
Computer vision at this time was effective in specific tasks, but lacked generalization, adaptability, and the ability to deal with complex variations in real-world images.
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
