Artificial intelligence has many types, one of the most powerful and compelling type is Computer vision, often abbreviated as a CV. CV is a field of study that seeks to replace and automate tasks that the human visual system can do. Until recently, computer vision only worked in a limited capacity.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to surpass humans in some tasks related to detecting and labeling objects. One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better.
Computer Vision and Image Processing
Computer vision is distinct from image processing.
Image processing is the process of creating a new image from an existing image, typically simplifying or enhancing the content in some way. It is a type of digital signal processing and is not concerned with understanding the content of an image.
A given computer vision system may require image processing to be applied to raw input, e.g. pre-processing images.
Examples of image processing include:
Normalizing photo-metric properties of the image, such as brightness or color.
Cropping the bounds of the image, such as centering an object in a photograph.
Removing digital noise from an image, such as digital artifacts from low light levels.
Challenges in Computer Vision
Helping computers be able to see has turned out to be very hard. Initially, it was believed to be a trivially simple problem that could be solved by a student connecting a camera to a computer. After decades of research, “computer vision” remains unsolved, at least in terms of meeting the capabilities of human vision.
One reason is that we don’t have a strong grasp of how human vision works. Studying biological vision requires an understanding of the perception organs like the eyes, as well as the interpretation of the perception within the brain. Much progress has been made, both in charting the process and in terms of discovering the tricks and shortcuts used by the system, although like any study that involves the brain, there is a long way to go. Another reason why it is such a challenging problem is because of the complexity inherent in the visual world.
A given object may be seen from any orientation, in any lighting conditions, with any type of occlusion from other objects, and so on. A true vision system must be able to “see” in any of an infinite number of scenes and still extract something meaningful.
Tasks in Computer Vision
The 2010 textbook on computer vision titled “Computer Vision: Algorithms and Applications” provides a list of some high-level problems where we have seen success with computer vision.
Optical character recognition (OCR)
Retail (e.g. automated checkouts)
3D model building (photogrammetry)
Match move (e.g. merging CGI with live actors in movies)
Motion capture (mocap)
Fingerprint recognition and biometrics
It is a broad area of study with many specialized tasks and techniques, as well as specializations to target application domains.
CV in Healthcare
Computer vision has also been an important part of advances in health-tech. For example, Computer vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans.
Despite the recent progress, and multiple healthcare institutions and enterprises that have found ways to apply CV systems, powered by CNNs, to real-world problems, we’re still not even close to solving computer vision.
So, Will this trend stopped in the future or there is a day we will finally have a solution for computer vision?