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:

• 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

#2: Optical Character Recognition (OCR)

Optical Character Recognition (OCR) is a unique implementation of computer vision that solves a variety of domain-specific tasks. OCR aims to detect and extract letters, numbers, and other characters from input images.

Use Cases

Google Lens uses OCR to let customers translate foreign languages from photos and extract text from images or Google searches. OCR technology also makes digitizing legacy media simple, pulling text from scans of newspapers, magazines, and books. Previously, universities struggled to digitize more obscure documents like Tibetan Buddhist religious texts, but modern OCR technology makes it simple to extract text from non-standard language documents.

Financial institutions use OCR for quality-of-life improvements for customers, such as letting customers extract their International Bank Account Number (IBAN) from documents or scanning images of checks so that it isn’t necessary to go into the bank to make a deposit. Some apps can scan a debit or credit card to input the payment details, so you don’t have to tediously type in all of your payment information at the checkout window.

Governments commonly utilize OCR to cut down on processing times at country borders or to identify and register documents. The machine-readable zone on modern passports and driver’s licenses is compatible with OCR systems in government and commercial settings.

#3: Facial Recognition

Similar to object recognition, facial recognition aims to identify human facial features in an image using computer vision. Classical computer vision methods utilized “Haar-like features” to calculate the segments between facial features, but modern facial recognition implementations depend on artificial intelligence much in the same way that AI is used for object recognition.

Use Cases

Facial recognition technology is crucial to security applications since it helps to prevent mobile and web application vulnerabilities. Countless Apple iPhone users rely on Apple’s Face ID technology for biometric authentication to unlock their phones.

Retailers have deployed similar implementations to identify known shoplifters. A real-time scanner grabs customers’ faces from a security camera stream and cross-references a database of known criminals. This same technology helps find missing children by pulling from law enforcement databases.

Facial recognition can also help you with the following tasks:

• Security and access control
• Identity verification
• Employee tracking
• Patient screening procedures in healthcare
• Identifying and tracking criminals

Next-generation facial recognition software can even look at postures, gestures, and facial expressions to determine whether or not a customer might be cheating at a casino. Gait analysis bundled with the same security software can also help detect criminals based on the unique pattern of their footsteps and stride, as many criminals hide from facial recognition by wearing masks.

#4: Image Restoration and Scene Reconstruction

Computer vision technology also makes it possible to restore heavily degraded archival footage and images which can be a critical business technique. Unlike simple cases where it’s enough to remove noise from a photo, computer vision can help with more damaged images that require serious changes and detailed analysis. Damaged parts of the image are usually filled using generative models that evaluate what the photo broadcasts.

Use Cases

In addition to restoring images and video, modern neural networks can reconstruct 3D scenes merely by scanning objects in a photograph. Archaeologists, forensic specialists, environmental scientists, and many other professionals utilize Scene Reconstruction, a game-changing computer vision paradigm. Projects like RetrievalFuse are able to construct panoptic 3D scenes from a single RGB image.

#5: Human Pose Estimation

Pose estimation aims to emulate human vision capabilities, specifically to identify poses and gestures in images and videos. Some of the earliest examples of advanced Human Pose Estimation turned up in big-budget motion pictures such as The Lord of the Rings by Peter Jackson. As computational resources scale up over time, pose estimation will feature in many different products.

Use Cases

In security applications, Pose Estimation helps identify potential troublemakers by analyzing their gait where Facial Recognition is infeasible. Computer vision can help detect shoplifting in real-time by analyzing body gestures. The system can distinguish between normal shopping behavior and suspicious behavior such as taking an item and hiding it in a pocket or coat. When suspicious behavior is detected, managers get alerts and can quickly respond to the situation before the thief leaves the store.

Here are some ways to use pose estimation in your business:

• Analysis of rehabilitation measures
• Development of AI-powered fitness coaching applications
• Recognition of the position of the human body in space to improve applications of augmented reality
• Game character animation
• Analysis of people’s activity in stores and shopping centers

Though pose estimation was once a massive computational challenge, innovations in cloud computing and hardware put the technology within reach of more companies.

The Sky is the Limit

Object detection, facial recognition, scene reconstruction, image restoration, and human pose estimation are just a handful of different implementations of computer vision technology. Because of the sheer power of next-generation AI, the chances are good that whatever industry your business occupies, computer vision offers unique benefits that can put your company ahead of the competition. From recreating full-depth three-dimensional models from crime scene photos to recognizing imperfections in mass-produced products on the factory line, computer vision continues to alter the way that everyone does business.