AI at the Edge: Transforming Machine Vision into Reality

The buzz about artificial intelligence has been growing strong compared to the past years, now its potential has been unleashed and businesses started to reap the benefits of this intelligent technology. Latest update on this is one of the most difficult challenges in AI: Making the device understand what they see. A vision to the machine.

Vision is a primary sense and one of the main mediums in which we live our lives, now it is for machines. As devices takes integral part in our daily lives, we can notice an increasing number of applications fail without adequate visual capabilities. Machine vision is the trending part of AI that aims to give machines a sight comparable to our own, but why would a machine need to see? How is it possible to give vision to a machine with Machine learning and Artificial intelligence!! See to the below ideology behind this visual realm

To solve complex tasks aggressively with minimal errors the “Machine vision” concept came into existence. AI’s applicability in machine vision relies on the affiliated branches of machine learning and more so, deep learning help machines identify and understand images from the real world. This is carried over by teaching the machine learning algorithms to computers to recognize features of an object. Say for example: The computer learns that if something is round and red, it’s an apple. Then a tomato is introduced, and so on and so forth. The machine (computer) continually understands and modify its model based on new information and assign a predictive value to each model, indicating the degree of confidence that an object is one thing over another

Areas where Machine vision find its importance

• Machine Vision inspection takes a crucial part in achieving 100% quality control in diverse industries, manufacturing industry on the top gets greatly benefited. Machine vision inspection reduces costs and ensures a high level of customer satisfaction. This effort is carried out by Machines and making it understand the continued adoption of technologies like neural networks and specialized machine vision hardware, now by this we are rapidly closing the gap between human and machine vision.

• Face Recognition system with Deep Learning based approach significantly improves the recognition based on traditional computer vision techniques. With this advancement recognition rate improved to over 95% with a false positive rate of 1 in 10,000 also accurately detects spoof attacks based on a person’s picture.

• Several custom algorithms are being used in a medical device for accurately measuring the power of the corrective lens. Medical devices with a significant imaging component have very stringent algorithm requirements when it comes to accuracy and robustness. These algorithms involves object detection, real-time calibration and robust feature extraction

In the future soon, we may start to see robots with visual capabilities going above and beyond our own, enabling them to carry out numerous complex tasks and operate completely autonomously within our society. Stay tuned to our blogs and get updates on recent technologies at your finger tips