Digital image processing has emerged in recent times as one of the trends in the imaging domain along with computer vision and pattern recognition. This technology has extended its reach in almost every field that we can think of, right from agriculture to car automation.
Despite the terms of digital image processing and computer vision are used interchangeably, there is indeed a difference between them. Digital image processing refers to the process by which we manipulate digital images through some mathematical functions and transformations. While we use the term computer vision when we apply digital image processing in conjunction with machine learning to recognize the patterns or interpret images.
The aim of this technology is to mimic the working of the human eye by digitally capturing and understanding images.
Areas of implementation
Medical Image Processing:
Since visual assessment is important in healthcare; advancements in computer vision along with machine intelligence have assisted many in this field. Using this technology, you can shave off diagnosis time, reduce cost, and enable early detection of many diseases. Some of the computer vision related activities that can be done in this field include segmentation of bones, analysis of heart images, density measurement of cartilage tissue, 3D reconstruction of organs, classification of cells and chromosomes and many more. For example, Microsoft’s Inner Eye is a tool that helps to identify and analyze 3D images of cancerous tumors.
Precision agriculture is an approach that relies on agtech companies to manage crops. Robotic drones and satellites provide farmers with real-time images of their crops. By implementing robotic sensing in fields, you can automate the processes like planting, weeding, and harvesting. Tractors and other vehicles can also be automated by relying on this technology for guidance. An Indian start-up Imago AI is building visual technologies that can help in increasing crop yields and reducing food waste.
Vehicles powered with vision and artificial intelligence will be much more capable than humans in driving efficiently and accurately. Autonomous vehicles require cameras in order to observe their environment and make decisions based on it. For an autonomous vehicle, it is vital to correctly classify the objects around it. Mistakes in this aspect could prove to be fatal. Since object detection hasn't reached its peak of accuracy, self-driving cars are still years away from consumer adoption.
Endeavors such as the Google Art Project deal with conserving, documenting, distributing, and restoring paintings by creating accurate digital copies that are free from imperfections caused by bad lighting or setup. Most art museums also archive their collections in this manner. Image processing is also helpful in identifying the artist of a certain painting, by analyzing brushwork and other minute details which might be missed by a person. The history behind a painting or sculpture can also be revealed by detecting patterns and the condition of the material used for creating that piece of art.
Facial recognition has been one of the most popular ways in which this technology has been applied, right from social media platforms like Facebook to Apple Animoji. It is one of the biometric methods used for authentication and identification, like the Face ID technology that allows users to unlock their smartphones with their “faceprint”. A tiny device called the OrCam MyMe uses its proprietary facial recognition technology to help you remember faces by associating names with them.
This technology has turned out to be one of the most important aspects of surveillance in many parts of the world. China has become one of the pioneers in this field and has been using it to keep an eye on its citizens. They aim to create a facial recognition system that can identify a person within three seconds, with 90 percent accuracy. Multiple parties have expressed concerns regarding their implications with respect to privacy. Microsoft has even adopted a code of conduct regarding this matter and encouraged other companies to do the same.
Current research on this subject is focused on activities such as creating efficient noise removal methods, improving the accuracy of object detection, and making new algorithms that would lessen the complexity and computing power of these processes. The image processing journey has been incredibly exciting, that its future will be as captivating as science fiction.