Our generation has witnessed an emerging shift in how much a machine can be involved in human lives. Using a computer to send Email, text to making video calls, chatting on multiple devices and now chatting with a machine. In no time machines were learning to understand the human behaviour by the text and now we have amazingly futuristic methods for allowing machines to read images, understand what’s happening in a video. There have been revolutionary changes and developments on “how a human operates a machine” and now, “how a machine operates itself”.
We came past the era where the content of an image or a video was limited to what was described or provided by the person uploading it, meaning the Meta Description. The need was, for the machines to See and recognize the content. And this is where, Computer Vision jumps in. Lets start by understanding what is computer vision.
Wikipedia Defines: “Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos.” Simply stating, Computer Vision aims at enabling machines to learn and implement the task of human visual system. To understand how human brain works, how it sees an object and how it relates it to an object or past information. To have a human perspective.
Computer Vision falls under Artificial Intelligence category and is one of the most powerful way to empower machines involving various contexts.
Now that we have understood what Computer Vision is, let us understand how it is actually implemented.
When a large amount of images is fed, for example pictures of a dog, the machine will try and learn from the image. Identifying a single object, the colour and the shape of the object, distance between multiple objects, borders for objects and the result will accumulate a simple definition of dog for the machine. For every next time, a picture of dog is uploaded, the machine will connect the dots, the patterns that it learned from the training images and tell us if it is a dog or not.
Object recognition is the basis of computer vision but also an issue. Having an image with clearly visible objects, acceptable angle, colouring, etc. is quite difficult. Providing unclear images to machine will lead to unexpected results.
Advanced Algorithms like Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, variety of Auto Encoders and many more, are been improved and implemented on daily basis.
These two distinct somewhere on a thin line. The process of generating new image using an existing image is what we define as Image Processing. Multiple filters can be added; Image can be manipulated in order to improve the quality of image. Now this output image can be used for better understanding of the images using computer vision. Majorly, Image Processing does not aim to understand the content of the image while computer vision has to. Image processing is responsible for manipulating the image in way to let the machine identify the objects and connect the dots.
Although there are many factors contributing to improvements and ground breaking changes in the field of Computer Vision, availability of training data in massive amount, is primary.
Recent developments resulting in advance Neural Networks and Deep Learning algorithms have pushed the limits, producing State-of-the-art Algorithms with better outputs. Added computation power and point accuracy with latest algorithms has made an impact on computer vision.
Being a sub field of Artificial Intelligence, Computer Vision comes with and provide solutions to high quality products. To recognize objects in a picture was good but specifically identifying a face, along with being able to add filters is a big leap. Using Face Detection techniques, Facebook and Snapchat detects live faces and improves the quality of pictures with filters.
When we use google for Image Search, it simply understands the content of the image we pass, learning the objects in the picture, it tries to match the objects and the results are purely based content matching. When we talk about Image Classification, Convolutional Neural Networks (CNNs) is the top pic and while other models require enough training and inputs, CNN does it all by itself. A CNN algorithm simply takes an image as input, recognize the objects inside and assign importance and finally compares the objects identified.
Surveillance Cameras in public places can now keep an eye on suspicious behaviour and notify. Security features like Biometrics, Face matching and IRIS are setting new limits for security.
Self-Driving cars are the future and various aspects of computer vision hold keys to improve the driverless car. Medical Imaging, Optical Character Recognition, machine inspection, 3D Model Building, Motion Detection, healthcare etc. are few of many applications.
Computer Vision and various sub categories will have drastic changes in future, and will surely lead to betterment of services. Along with increased capacity, future algorithms will be easy to train on much massive data. Intervention of other technologies of same sub family will lead to surprising results. Very important role will be played by computer vision for development of computer super intelligence and Artificial General Intelligence. At the cost of humans being observed at every point, from places we visit to dishes we eat and words we use, we will have amazing future developments in computer vision.
The link between how human thinks and how machine processes, will definitely be strengthen. DataToBiz have been working with many corporates to shape up their computer vision products/services. Contact our expert to discuss how AI can add value to transform your business.