Not everything is ChatGPT: Computer Vision
In recent months, there has been much talk about artificial intelligence. Some approach it with rejection, others with anticipation, and many with skepticism. While it is true that significant progress has been made in recent times, artificial intelligence is not new and is not solely focused on generative AI.
Today, we want to introduce you to our platform – Golineuro AI – and discuss artificial intelligence applied to computer vision.
Computer vision (CV) is an area of AI that focuses on enabling computers to identify and process images and videos in a way similar to humans.
Until recently, computer vision had limited functionality, but thanks to advances in deep learning (AI), this field has made great strides in recent years and is revolutionizing various industries.
How does it work? With Convolutional Neural Networks (CNNs).
Its operation primarily involves extracting features from different layers that make up an image. Suppose we have an image of a boat, along with a set of labels such as “human,” “vehicle,” and “plant.” The computer vision technology, after analyzing the layer of the image containing the shape of the boat, will classify and assign the boat image to the “vehicle” label. This process is called image classification.
Once the system has classified all boat images, we can access data such as how many vehicles it has classified, but not the original images of the boats, which were lost once the system classified them. In other words, only the data is stored, not the images.
Numerous classifications can be created, allowing access to information such as how many vehicles there are or how many were red. For this, a well-generated taxonomy (organization) is important for later data analysis.
The Convolutional Neural Networks (CNNs) are responsible for performing this analysis and classification, with their two main applications in computer vision being classification and detection.
Self-learning
Self-learning is a crucial aspect of classification models. The extracted information for each taxonomy can be reused for others. Thus, the neural network uses its knowledge base for each convolution, making it more accurate with increased usage, as it leverages its previous knowledge to improve.
In the case of our platform, Golineuro AI, the neural networks have acquired knowledge in crowded environments such as cities, trade shows, large surfaces, and shopping malls, with over eight years of daily use in these settings, providing extensive knowledge.
If you want to learn more, feel free to contact us.
