What is Computer Vision?
Computer Vision is the field of study that allows computers to understand the physical world like us humans. It trains and enables computers to find meaningful information from digital images, videos and any other form of visual input. The purpose of Computer Vision in AI is to recognize patterns in the visual data at a pixel level and use those patterns to determine the contents of other images, and automate systems that can interpret visual data in the same manner as people do, using Computer Vision software with special algorithms.
Here are a few common tasks that Computer Vision systems can be used for:
- Object Classification. The system parses digitalized visual information and classifies objects in a photo or video to the defined category. For example, the system can identify an orange among all other fruits or other objects in an image by separating it on the basis of its characteristic features like roundness and orange color.
- Object Identification. The system parses digital visual information and identifies a particular object in a photo or video. For example, the system can identify a specific breed of cat amongst other cats, or a specific breed of dog amongst other dog breeds.
- Object Tracking. The system processes video to find the object (or objects) that matches the predetermined search criteria and tracks its movement.
How does Computer Vision Work?
If AI enables computers to replicate the human brain, Computer Vision enables computers to replicate, observe and understand human vision. But how is it that our brain processes visual object recognition? One of the popular hypothesis states that our brains pick up information through patterns from our surroundings and rely on them to decode individual objects and track them. This concept is used to create Computer Vision systems.
These systems require a larger amount of data to be able to decipher information through digital visuals. It repeatedly runs analyses of data over and over until it discerns distinctions and ultimately recognizes objects.
Deep learning and convolutional neural networks (CNN) are the two main methods used to achieve this goal.
Machine learning uses algorithmic models that enable a computer to automatically learn and teach itself about the context of visual data. If the data fed through the model is large enough, the computer using the model will learn to differentiate, and distinguish data from one image to another. Algorithms enable the machine to learn by itself, rather than getting manual human input each time it needs to learn.
A CNN helps a machine learning and deep learning model by breaking images down into sections and then further dividing them into pixels. Those pixels are then given tags or labels which are used to perform convolutions, a mathematical operation that produces a third function using two functions, and makes predictions about what it is observing. With each cycle, the neural network runs convolutions and checks the accuracy of its predictions in a series of iterations. Once the accuracy improves and it starts perceiving and identifying correctly, its functionality becomes closer to that of human vision.
Where is Computer Vision Being Used Today?
Computer Vision is a rapidly expanding field in real world applications, as well as research. Its research is more directly impacting the world, technologies that were destined to take 5 to 10 years are being developed, launched and implemented today. Computer Vision examples present in the world today that can be observed are:
Facial recognition programs rely heavily on Computer Vision models to recognize faces of individuals in photographs and video feed. Use of facial recognition is growing rapidly, in order to verify the identity of the people entering a facility or using a device. Even social networking applications use facial recognition for both user detection, and user tagging. Law enforcement agencies use facial recognition software to track down criminals using surveillance footage.
Self driving cars use Computer Vision to understand the surrounding environment. Multiple cameras are used to record visual data which is processed by its algorithms that analyze the photos with perfect synchronization to locate road edges, decipher signposts, and see other vehicles, obstacles, and people. Using this, autonomous vehicles can navigate streets and highways on their own.
Healthcare technology has improved by leaps and bounds thanks to Computer Vision. Automating the process of looking for indications in X-Ray or MRI scans for malignant moles on a person’s skin or locating indicators in an X-Ray or MRI scan is only one of the many applications of Computer Vision algorithms in the medical field.
Quality in production of products is improving using the help of Computer Vision. Inspections of the quality of the product, identifying faults and defects help automate the inspection process and save time while preserving quality.
How Will it Impact the Future?
In the future, Computer Vision will provide a broader range of functions and we will be able to train models effortlessly and easily. As we progress forward in time the possibilities and capabilities of Computer Vision seem endless.
Continued innovations and refinement of Computer Vision technology will make it easier to train models with less data than the present requirement and thus improve our ability to analyze images with more efficiency. Computer Vision would be able to work in conjunction with other technologies or subfields of AI to create more practical applications. For example, an application that visualizes and understands objects in the surroundings can use computer generated voice instructions to guide visually challenged people. An application like that can be created using the help of Computer Vision and natural language generation (NLG).
Another future aspect of Computer Vision is reasoning based on the common sense of humans. Understanding, visualizing, storing data and answering questions based on basic human logic from images and videos could be the next big advancement in it. Understanding and deciphering what is captured in an image is only the first step towards common sense reasoning. The next step would be utilizing visual common sense reasoning and move past just identifying the types of objects in image data. In future the machine is expected to provide additional information from videos and images such as:
- What is there?
- Who is the action being performed on?
- Who is there?
- Which climatic conditions are affecting their activity?
Fascinating Times Ahead for Computer Vision
When we observe the capabilities of Computer Vision today, it is almost incredible what human ingenuity has already achieved. However, the future of its promises to be even stranger than fiction, and can become the true reflection of the human spirit: exploring, inventing, going into uncharted territory.