Computer vision technology simulates the visual perception of living organisms, including humans. Used in domains including AI, machine learning, computer science, and mathematics, computer vision allows machines to collect, interpret, and understand visual data.
A typical computer vision system takes as input digital visual data captured by sensors such as cameras, LiDARs, and radars, and then processes and transfers the data to a machine learning or deep learning model for further interpretation.
Computer vision technology is shaping the future of applications such as digital library organization, security systems, autonomous robots, self-driving cars, and more.
Initially computer vision applications were restricted to optical character recognition, but now the technology has made its way into other areas, including defense, manufacturing, automotive, robotics (for self-driving cars), biology, and retail. Some of the most popular applications of computer vision include:
Data collection, data management, and data labeling are all challenges when implementing computer vision systems.
Machine learning algorithms, especially those used for deep learning, require large amounts of data. In some use cases, such as medical imaging applications, acquiring specialized data in large quantities can be costly and expensive. Moreover, it's not just about volume. Machine learning teams must use a variety of scenarios to account for edge cases, such as collecting data during daytime, night time, and in adverse weather conditions, especially in the case of autonomous vehicles.
Once you've collected a large volume of data, mining all the raw data to find specific scenarios that actually improve model performance can be a challenge. Most teams either manually parse through their data or sample randomly.
Once you have collected data and selected what data to label, you need to get enough of it labeled at a high enough quality for your application. Images collected in the real world can be blurry, some objects may be occluded, and poor lighting can also make images more challenging to interpret.
Neural networks, the machine learning models inspired by the working of the human brain and their extension in the form of deep learning algorithms, have been game changers in the field of computer vision. With these technologies, applications considered challenging or complex, such as object recognition, medical diagnosis from images, and autonomous robots, can be developed successfully.
Deep learning is computationally expensive, both in terms of CPU resources and memory, and that has led to the rising popularity of cloud-based services. Edge devices in computer vision technology are also emerging; edge computing refers to processing and computing at the source location of data generation, e.g., processing real-time sensor data for autonomous vehicles or self-driving cars.
Computer vision is heading towards the following goals in the short term:
Longer-term research efforts for enhancing computer vision technology include:
Practitioners have barely scratched the surface of the possible future applications, and the world is just starting to see the benefits of computer vision technology in day-to-day life.
Any organization working with image data can reap the benefits of computer vision and find automated solutions that are cost effective and that save time.
The Conference on Computer Vision and Pattern Recognition is one of the leading conferences on computer vision, where the latest research is presented. Here are a few notable papers from the 2021 conference: