The History of Computer Vision on the Path to AGI

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In humans and other animals, vision involves the detection of light patterns from the environment and their interpretation as images. This complex process relies on the integration of sensory inputs by the eyes and their subsequent processing by the brain, interconnected by the nervous system. A resulting intricate network enables humans to filter and prioritize stimuli from a constant influx of sensory information, transforming light patterns into coherent, meaningful perceptions.

We will see in the future episodes, that researchers tried very different approaches to teach machines to see, perceive, and interpret images. Don't be fooled (or upset) by the anthropomorphizing wording. Despite the field's advancements, current computer vision techniques remain fundamentally different from human vision. They are based on sophisticated algorithms and models that approximate visual processing, rather than duplicating the biological complexity of human sight.

It's an irony, but even with such advancements in language processing, we still can't process with the right language to avoid anthropomorphizing machines.

How does it work?

Now, let's dive into the technical aspects of computer vision. Scientists have long been fascinated with the idea of translating the vision ability of humans into something computer-readable. From the outset, researchers faced the critical task of determining how to describe and identify objects within an image to a computer, including deciding which features were most essential for recognition. It may seem to be very straightforward for people as they're wired for that but for machines... For computers, an image is just a collection of pixels, a binary code that is stored in computer memory.

Teaching a machine to 'see' is complex. It's not just about the capture of images, but the interpretation of what those images mean. Computer vision breaks this down into several key steps:

• Object Recognition: At its most basic level, CV systems learn to identify what's in an image; cars, people, animals, furniture, and so on. This is a building block for further understanding.

• Semantic Segmentation: Taking it further, CV can learn to label every single pixel in an image, classifying them as belonging to certain object categories. Imagine an image colored not with light and shadow, but with labels – this is how a sophisticated CV system might 'see'.

• Visual Relationships: Real intelligence understands not just objects, but how they relate. Is a person standing beside a car, or about to get into it? CV algorithms that grasp these spatial and contextual relationships bring an AI closer to comprehensive scene understanding.

The Process of ‘Seeing’

This process can be broadly categorized into several steps, mirroring aspects of human visual processing:

1. Image Acquisition: The journey starts with capturing an image or video, and converting the visual information into a digital format that a computer can process.

2. Preprocessing: Here, the digital image is refined, enhancing its quality through techniques like noise reduction and contrast adjustment to ensure accurate analysis.

3. Feature Extraction: The essence of computer vision lies in identifying and isolating specific features within an image. These features could be edges, textures, colors, or shapes that are critical for the subsequent steps of object detection and recognition.

4. Detection/Segmentation: This phase involves differentiating objects from the background (segmentation) or identifying particular objects within the image (detection). It's a pivotal step in making sense of the visual data by pinpointing areas of interest.

5. Classification/Recognition: At this stage, the identified objects are classified or recognized. This means assigning a label to an object based on its features, essentially allowing the system to 'understand' what it is seeing.

6. Post-processing: The final touch includes refining the output, considering the broader context, or integrating additional data sources to enhance the accuracy and reliability of the interpretation.

What made computer vision possible?

Computer vision mirrors human visual processes but operates under the constraints of technological infancy. While human vision benefits from a lifetime of contextual learning – distinguishing objects, gauging distances, and recognizing movement – computer vision seeks to replicate these abilities through data, algorithms, and hardware. Despite starting from scratch, these systems can quickly outpace human capability by processing and analyzing thousands of items or situations per minute, identifying nuances and defects that might escape the human eye.

At the heart of computer vision's functionality is an insatiable appetite for data. It thrives on vast datasets, processing and reprocessing this information to distinguish between different visual elements and recognize specific objects or features. This learning process is powered by two pivotal technologies:

• Deep Learning: A subset of machine learning, deep learning uses algorithmic models that enable systems to self-educate on the context and content of visual data. By feeding these models a continuous stream of data, the computer learns to differentiate and identify images through self-guided algorithms.

• Convolutional Neural Network (CNN): CNNs are crucial for breaking down images into manageable pieces – pixels tagged with labels. These labels assist in performing mathematical operations (convolutions) that iteratively predict the content of the images. This process, mirroring the gradual refinement of human sight from recognizing basic shapes to detailed objects, allows the computer to "see" in a manner akin to human vision.

The challenge of imparting basic human abilities, such as sensory and perceptual skills innate to even a one-year-old, to a computer, was crystallized by Hans Moravec, Rodney Brooks, and Marvin Minsky through a concept known as Moravec's Paradox. This principle posits that replicating these seemingly simple skills demands far more computational resources than solving problems humans find complex.

One reason for that is that we barely know ourselves.

The crux of the issue lies in reverse engineering a process that, for us, is as natural as breathing yet as enigmatic as the universe itself. Articulating a profoundly complex problem for algorithmic treatment becomes much simpler when the underlying algorithm is known – consider, for instance, devising an optimal strategy using game theory. While such an algorithm might be comprehensible to only a select few due to its complexity, it paradoxically requires significantly fewer computational resources for a computer to execute.

We will guide you through all the ups and downs of the research and development process from the late 1950s in this historical series about CV.

The areas that can not exist without computer vision

This accelerated learning and operational capability is what makes computer vision invaluable across various industries – from energy and utilities to automotive manufacturing. And that effects →

Computer vision market

To know about computer vision is also important because the market for it is … large. It is experiencing substantial growth, with projections varying significantly between different sources:

• Statista: With the size projected to reach $26.26 bn in 2024, expected to grow at a CAGR of 11.69% until 2030 to reach $50.97 bn.

• Grand View Research: With a size of $14.10 bn in 2022, growing at a CAGR of 19.6% until 2030 to reach $58.29 bn.

• Allied Market Research: with the size valued at $15 bn in 2022, growing at a CAGR of 18.7% until 2032 to reach $82.1 bn by 2032.