The Mission for Machine Vision and Object Detection Recognition… The Acceleration of Visual Acuity, Seeing Beyond the Object

by | Tuesday 12th February 2019

When it comes to artificial intelligence and machine learning, computer vision has made significant progress in identifying and interpreting a range of physical objects over the last couple of years; computer vision is an emerging technology focused on detecting and processing images, similar to human vision. It uses artificial intelligence to determine the image based on observation (of the environment) and interprets the object and next sequence of actions.

The Perceptual Vehicle

Computer vision will have a profound impact on observing its surroundings and helping humans anticipate the unexpected. Just consider the advancements in autonomous vehicles and their ability to recognize their surroundings and act accordingly. For example, they can stop automatically when obstacles are observed.

During CES this year, AutoX, based in San Jose, California, was prominently featured showcasing its latest self-driving real-time recognition and deep learning technology. Its xFusion was displayed, a driving perception system that merges camera and lidar technology to accurately identify the type and location of objects, including the precise identification of small objects, such as pedestrians walking the street.

Machine Vision Inference

Why is this important? Similar to voice and contextual processing, the next phase of development is computer vision, combining machine vision systems with artificial intelligence to make sense of the objects with adaptive reasoning solutions. That will require deep learning technology to identify objects with greater ease so machines can be trained through a set of programs in efforts to increase rational (and meaningful) response accuracy. The mission for machine vision is the ability to detect, follow, avoid, and operate using deep learning networks to facilitate the basic need for commonsense.

Applications and Aptitude

Computer vision is useful in various applications such as facial recognition, gesture control, image restoration, character recognition, medical image analysis and many more. The computer vision technology is deployed in various industry verticals such as automotive, healthcare, financial, manufacturing, consumer electronics, robots, drones, augmented and virtual reality, gaming, and entertainment. Both the consumer and enterprise space will demand computer vision applications and analysis equipped with deep learning algorithms, visionary sensors and chipsets to drive intuitive machines that can see beyond the object, all helping to accelerate the growth of the computer vision market.

Machines are only just beginning to understand the human visual experience as they move beyond pattern recognition and enter the complex contextual reasoning real world application. The distinction between the two will be clear.


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Contact the author:

Dennis Goldenson