Understanding the Machine Learning technology that is propelling the future
Any computing system fundamentally works on the basic concepts of input and output. Whether it is a rudimentary calculator, our all-requirements-met smartphone, a NASA supercomputer predicting the effects of events occurring thousands of light-years away, or a robot-like J.A.R.V.I.S. helping us defend the planet, it’s always a response to a stimulus — much like how we humans operate — and the algorithms which we create teach the process for the same. The specifications of the processing tools determine how accurate, quick, and advanced the output information can be.
Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos. To put it in a very simple manner, computer vision advances the input (output) steps by reading (reporting) information at the same visual level as a person and therefore removing the need for translation into machine language (vice versa). Naturally, computer vision techniques have the potential for a higher level of understanding and application in the human world.
While computer vision techniques have been around since the 1960s, it wasn’t till recently that they picked up the pace to become very powerful tools. Advancements in Machine Learning, as well as increasingly capable storage and computational tools, have enabled the rise in the stock of Computer Vision methods.
What follows is also an explanation of how Artificial Intelligence is born.
Machines interpret images as a collection of individual pixels, with each colored pixel being a combination of three different numbers. The total number of pixels is called the image resolution, and higher resolutions become bigger sizes (storage size). Any algorithm which tries to process images needs to be capable of crunching large numbers, which is why the progress in this field is tangential to advancement in computational ability.
The building blocks of Computer Vision are the following two:
As is evident from the names, they stand for figuring out distinct objects in images (Detection) and recognizing objects with specific names (Identification).
These techniques are implemented through several methods, with algorithms of increasing complexity providing increasingly advanced results.
The previous section explains the architecture behind a computer’s understanding of images. Before a computer can perform the required output function, it is trained to predict such results based on data that is known to be relevant and at the same time accurate — this is called Training Data. An algorithm is a set of guidelines that defines the process by which a computer achieves the output — the closer the output is to the expected result, the better the algorithm. This training forms what is called Machine Learning.
This article is not going to delve into the details of Machine Learning (or Deep Learning, Neural Networks, etc.) algorithms and tools — basically, they are the programming techniques that work through the Training Data. Rather, we will proceed now to elaborate on the tools that are used to prepare the Training Data required for such an algorithm to feed on — this is where Bridged’s expertise comes into the picture.
For a computer to understand images, the training data needs to be labeled and presented in a language that the computer would eventually learn and implement by itself — thus becoming artificially intelligent.
The labeling methods used to generate usable training data are called Annotation techniques, or for Computer Vision, Image Annotation. Each of these methods uses a different type of labeling, usable for various end-goals.
At Bridged AI, as reliable players for artificial intelligence and machine learning training data, we offer a range of image annotation services, few of which are listed below:
2D/3D Bounding Boxes
Drawing rectangles or cuboids around objects in an image and labeling them to different classes.
Marking points of interest in an object to define its identifiable features.
Drawing lines over objects and assigning a class to them.
Drawing polygonal boundaries around objects and class-labeling them accordingly.
Labeling images at a pixel level for a greater understanding and classification of objects.
Object tracking through multiple frames to estimate both spatial and temporal quantities.
Applications of Computer Vision
It would not be an exaggeration to say computer vision is driving modern technology like no other. It finds application in very many fields — from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries — and they are increasing by the day.
Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
The use of this powerful tool is not limited to just fancying photos. You can implement it to quickly sift through customer databases, or even for surveillance and security by identifying fraudsters.
Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
Augmented & Virtual Reality
Again, Computer Vision is central to creating limitless fantasy worlds within physical boundaries and augmenting our senses.
Optical Character Recognition
An AI system can be trained through Computer Vision to identify and read text from images and images of documents and use it for faster processing, filtering, and on-boarding.
Artificial Intelligence is the leading technology of the 21st century. While doomsday conspirators cry themselves hoarse about the potential destruction of the human race at the hands of AI robots, Bridged.co firmly believes that the various applications of AI that we see around us today are just like any other technological advancement, only better. Artificial Intelligence has only helped us in improving the quality of life while achieving unprecedented levels of automation and leaving us amazed at our own achievements at the same time. The Computer Vision mission has only just begun.
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