During the North American summer, a woman decided to go to a forest near her home to look for unconventional foods, such as plants and mushrooms. The practice, called foragingis quite common in southern states.
She thought she was picking mushrooms called chicken of the woodsan edible species. After lunch, her husband and children began to feel ill. The woman confused the species and actually took home jack-o'lanternsa poisonous and potentially fatal mushroom.
Recognizing mushrooms would have prevented health problems! Image recognition technology not only prevents confusion with mushrooms, but also identifies people, places and products. This article explores how it works. Ready to get started?
What is Image Recognition?
Image recognition, or computer vision, is a technological tool used to analyze, categorize and distinguish fragments or complete images.
Developed from facial recognition technology in the 1960s, recognition has reached images of objects, places and texts, even instances with a high level of detail.
For example, Chinese doctors have perfected the model of artificial intelligence (AI) model to evaluate organic tissue and find changes in color or texture.
What's the Difference Between Image and Face Recognition?
The main distinction between the recognition models is the training they both receive. While image recognition can be applied to any type of photo, illustration or art form, facial recognition is trained to distinguish people.
In order not to confuse people, the machine must be trained with a high volume of highly diverse images. But it also needs to be trained to understand that the same man can have long or short hair, a moustache, a beard, wear glasses or have tattoos.
In addition, facial recognition when used in motion-capture cameras needs to identify a person even with facial expressions. It's a major challenge that is still ongoing.
How does Image Recognition work?
Image recognition is a resource developed using AI, so to begin with, it is necessary to train the algorithms. This training is an important part of the process, because it lays the foundations that will guide the machine in its activities.
To complete this training, the user upload a set of images with various elements. The machine then processes the references and performs the three tasks we mentioned earlier.
- Analyze: the analysis stage is where the AI will evaluate each element of the image and determine what its fundamental characteristics are: shape, color, light, shadows, style and image quality;
- Categorize: The categorization stage is where the image is given a "label". The AI will determine whether it is a photo of a forest or an illustration of a boat, for example;
- Distinguish: e Finally, the next step is to compare the image with others. In this way, the AI can determine what they have in common and suggest similar results to the user.
Where do we use Image Recognition?
Perhaps the best-known application of image recognition is Google, but there are other platforms that also use this technology. We'll mention four that are worth mentioning.
Google Image Search
Have you ever thought about that? The image search you do on Google uses image recognition to present you with the best search results.
So, when you type in "blue car in a race", Google Images will only show those that have been analyzed and categorized for this subject.
Sometimes it will show other options with elements that you didn't include in the search, but which contain the central idea of the blue car or the race.
Google Lens
This Google app is image recognition technology at its most powerful. By opening the app, the user can point the camera at an object and immediately know what it is.
For this reason, Google Lens has become one of the tools most used by tourists in countries with languages they don't know, or the company in a museum to better understand the works on display.
It is also used by botanists to catalog plant species, people who want to find a product, or even car collectors who need to register the models they want.
Tineye
Tineye has become one of the most widely used platforms for fact-checkers. The high recognition capacity and a database of more than 64 billion images is an attraction for journalists.
Thus, it is possible to find images and trace both the place where they were first posted and the date on which they were published. This feature is important for identifying images that are used in inappropriate contexts or in a misleading way.
Tess AI
The Tess AI platform is one of the tools with image recognition technology.
Some generative models make it possible to use one image as a reference for producing others with the same elements. As a result, the applications become more varied.
Boost your design, paid ads, social networks, blog covers and much more. Explore for free the Tess AI platform to optimize your tasks and revolutionize your routine!
Conclusion
In this article we look at what this technology is, how it works and what its main functions are. We also talk a little about what facial recognition is and how it differs from image recognition.
Technology continues to evolve and we're still going to see a lot of new developments in this field. Consequently, you need to be up to date with the latest in the field of AI.
On Pareto blogyou'll find articles every week on what is dominating the world of artificial intelligence. Follow the new posts!
