07 Oct Computer Vision: Everything You to know about Computer Vision
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Computer vision is one of the most potent and compelling types of AI, which you’ve almost certainly experienced in many ways without even knowing. So here’s a look at what it is, how it works, and why it’s so impressive (and it’s only going to get better).
Computational vision is the field of computer science that focuses on replicating parts of the difficulty of the human graphic system and allowing computers to identify and process objects in images and videos the same way humans do. Until recently, computer vision worked only in a limited volume.
The field has made great leaps in recent years thanks to early artificial intelligence and innovations in deep learning and neural networks. As a result, it has outperformed humans in some tasks related to object detection and labeling.
One of the factors driving the increase in computer vision is the amount of data we generate today that remains used to train and improve computer vision.
The first computer vision experiments began in the 1950s and remained used commercially to distinguish between typed and handwritten text in the 1970s. Today computer vision applications have grown exponentially.
How Does Computer Vision Work?
One of the leading open questions in Neuroscience and Machine Learning is: How exactly does our brain work, and how can we approach that with our algorithms? Unfortunately, the reality is that there are very few functional and complete theories of brain computing; so level though Neural Networks are supposed to “mimic the way the brain works,” no one is quite sure if that is true.
The same paradox applies to computer vision: because we are undecided about how the brain and eyes method images, it is difficult to say how well the algorithms used in production approximate our internal mental processes.
At some level, computational vision is about pattern recognition. So one way to train a computer to understand visual data is to feed it images, lots of pictures, thousands, millions if possible, that have remained tagged, and then submit them to various software techniques, or algorithms, that allow the computer to search for patterns in all the elements that relate to those tags.
The Evolution of Computer Vision
Before the advent of deep learning, computational vision tasks were minimal and required a lot of manual coding and effort from developers and human operators. For example, if you want to achieve facial recognition, you must perform the following steps:
Create a database: you had to capture individual images of all the topics you wanted to crawl in a specific format.
Annotate images: Then, for each image, you would have to enter several key data points, such as the reserve between the eyes, the width of the nasal bridge, the distance between the upper lip and the nose, and dozens of other measures that define each person’s unique characteristics.
Capture new images: Next, you’ll need to capture unique shots, either from photos or video content. And then, you had to go through the measurement process again, marking the critical points in the image. You also had to consider the angle at which the picture remains taken.
After all this manual work, the application could finally compare the measurements of the new image with those stored in its database and tell you if it matched any of the profiles it was tracking. But unfortunately, there was very little automation involved, and most of the work remains b done manually. And the margin of mistake was still significant.
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How Long Does Computer Vision Take To Decipher an Image
In short, not much. That’s the key to why computer vision is so exciting: whereas in the past, even supercomputers could take days or weeks, or uniform months to review all the necessary calculations, today’s ultra-fast chips and related hardware, along with fast, reliable Internet and cloud networks, make the process very fast. In addition, once a crucial factor was the willingness of many large AI research firms to share their work with Facebook, Google, IBM, and Microsoft, particularly by openly hiring some of their machine-learning work.
Computer Vision – It allows others to develop their work instead of starting from scratch. As a result, the AI industry is making progress, and experiments that took weeks not too long ago to remain conducted could take 15 minutes today. And for many real-world applications of machine vision. This whole process happens continuously in microseconds, so a computer today can be what scientists call “situation-aware.
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