K
Kathleen Martin
Guest
Self-driven cars, traffic sign detection, facial recognition, and self-checkouts. What unites all of these progressive solutions is computer vision. Computer vision allows computers to extract information from raw images and open a lot of opportunities for more effective digitalization of business. Let’s take a look at how computer vision is disrupting various industries and what unique benefits it brings to help owners solve key business challenges.
#1: Object Detection
Traditional computer vision implementations used in-depth analysis of inputs and outputs. The typical flow in the case of old-school CV depended on image processing techniques like edge detection to identify and label objects in an image.
The advent of deep learning architecture in computer science caused a monumental shift from classical CV techniques like definition-based feature structures to AI-driven neural network analysis of imagery, which allows near-complete automation of extraction and classification of data in images. In simpler terms, artificial intelligence takes the programming out of the picture in favor of a less supervised approach in which the computer interprets the input data and trains itself to recognize the content of images.
Use Cases
When AI steps into fields like medical imaging, the computer leverages superior pattern recognition to identify subtle elements in raw images, such as whether or not cancer cells are present in minute amounts in an X-ray or MRI. Despite the fact that human interpretation and expertise are still needed to check the deductions of the machine, an additional layer of lightning-quick analysis helps supplement human intelligence and save lives.
As self-driving cars hit the road across the United States and many other countries, the CV field will see explosive growth. Autonomous vehicles can’t exist without computer vision. Since the vehicle’s onboard computer needs to make quick decisions about potential obstacles on the road, it depends on a highly optimized set of CV-based technologies.
It’s important to note that in areas such as medicine, security, manufacturing, etc., the transparency of how an AI-powered system makes a decision is crucial. This is where explainable AI comes into play. This technique allows for interpretation of the findings of the system in a way humans can understand and shows the reliability of a particular decision made by an AI algorithm.
Use computer vision to address the following business challenges:
#1: Object Detection
Traditional computer vision implementations used in-depth analysis of inputs and outputs. The typical flow in the case of old-school CV depended on image processing techniques like edge detection to identify and label objects in an image.
The advent of deep learning architecture in computer science caused a monumental shift from classical CV techniques like definition-based feature structures to AI-driven neural network analysis of imagery, which allows near-complete automation of extraction and classification of data in images. In simpler terms, artificial intelligence takes the programming out of the picture in favor of a less supervised approach in which the computer interprets the input data and trains itself to recognize the content of images.
Use Cases
When AI steps into fields like medical imaging, the computer leverages superior pattern recognition to identify subtle elements in raw images, such as whether or not cancer cells are present in minute amounts in an X-ray or MRI. Despite the fact that human interpretation and expertise are still needed to check the deductions of the machine, an additional layer of lightning-quick analysis helps supplement human intelligence and save lives.
As self-driving cars hit the road across the United States and many other countries, the CV field will see explosive growth. Autonomous vehicles can’t exist without computer vision. Since the vehicle’s onboard computer needs to make quick decisions about potential obstacles on the road, it depends on a highly optimized set of CV-based technologies.
It’s important to note that in areas such as medicine, security, manufacturing, etc., the transparency of how an AI-powered system makes a decision is crucial. This is where explainable AI comes into play. This technique allows for interpretation of the findings of the system in a way humans can understand and shows the reliability of a particular decision made by an AI algorithm.
Use computer vision to address the following business challenges:
- Public security (vehicle identification, weapon type recognition, locating suspicious objects, etc.)
- Sales automation and inventory management (identifying low-stock or misplaced items on the shelves, detecting empty shelves, performing quality control, product recognition for self-checkouts, etc.)
- Eliminating human error and preventing double-counting in the workflow
