Five ways computer vision can help solve business challenges

Self-driving cars, traffic sign detection, facial recognition and self-checkout. What brings all these advanced solutions together is computer vision.

Computer vision allows computers to extract information from raw images and opens up many opportunities for more efficient business digitization.

Let’s take a look at how computer vision is disrupting various industries and what unique benefits it brings to help owners solve critical business challenges.

1. Object Detection

Traditional computer vision implementation uses in-depth analysis of input and output. The typical workflow of old-school CV relies on image processing techniques such as edge detection to identify and label objects in images.

The emergence of deep learning architectures in computer science has led to a huge shift from classic CV techniques (such as based on defined feature structures) to AI-driven image neural network analysis, which enables the extraction and classification of data in images. Almost completely automated. Simply put, AI takes programming out of the picture, replacing it with a less supervised approach in which computers interpret input data and train themselves to recognize the content of images.

USE CASES

When AI enters fields such as medical imaging, computers use superior pattern recognition to identify subtle elements in raw images, such as the presence of trace amounts of cancer cells in an X-ray or MRI. While human interpretation and expertise are still needed to check the machine's inferences, the additional layer of lightning-fast analysis helps supplement human intelligence and save lives.

As self-driving cars hit the roads in the U.S. and many other countries, the CV space is poised for explosive growth. Self-driving cars cannot exist without computer vision. Because the vehicle's onboard computer needs to make quick decisions about potential obstacles on the road, it relies on a highly optimized set of CV-based techniques.

It’s important to note that in fields such as medicine, security, manufacturing, and more, transparency into how AI-driven systems make decisions is critical. This is where explainable AI comes into play. The technology allows the system’s findings to be explained in a way that humans can understand and shows the reliability of specific decisions made by AI algorithms.

Use computer vision to solve the following business challenges:

• Public security (vehicle identification, weapon type identification, suspicious object location, etc.).
• Sales automation and inventory management (identify low stock or misplaced items on shelves, detect empty shelves, perform quality control, product identification for self-checkout, etc.).
• Eliminate human error and prevent double counting in your workflow.

2. Optical Character Recognition (OCR)

Optical character recognition (OCR) is a unique implementation of computer vision that can solve a variety of domain-specific tasks. OCR is designed 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 it easy to digitize traditional media, extracting text from scans of newspapers, magazines and books. Previously, universities had difficulty digitizing more obscure documents such as Tibetan Buddhist religious texts, but modern OCR technology has made it simple to extract text from non-standard language files.

Financial institutions use OCR to improve their customers' quality of life, such as allowing customers to extract their International Bank Account Number (IBAN) from a document or scan a check image so they don't have to go to the bank to make a deposit. Some applications can scan and borrow money. Debit or credit card to enter payment details, so you don't have to tediously enter all your payment information at the checkout window.

Governments often use OCR to reduce processing times at national borders or to identify and register documents. The machine-readable areas on modern passports and driver's licenses are compatible with OCR systems in government and commercial environments.

3. Facial Recognition

Similar to object recognition, facial recognition aims to identify human facial features in images using computer vision. Classic computer vision methods utilize "Haar-like features" to count segments between facial features, but modern facial recognition implementations rely on artificial intelligence, just like artificial intelligence is used for object recognition.

USE CASES

Facial recognition technology is critical for security applications as it helps 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. Live scanners capture customers' faces from security camera streams and cross-reference them with databases of known criminals. The same technology helps find missing children by pulling from law enforcement databases.

Facial recognition can also help you complete the following tasks:

• Security and access control.
• Authentication.
• Employee tracking.
• Patient screening procedures in health care.
• Identify and track criminals.

Next-generation facial recognition software can even look at posture, hand gestures and facial expressions to determine if a customer might be cheating at the casino. Gait analysis bundled with the same security software can also help detect criminals based on their unique footsteps and stride patterns, as many criminals evade facial recognition by wearing masks.

4. Image recovery and scene reconstruction

Computer vision technology can also restore severely degraded archival footage and images, which can be a critical business technology. Unlike simple cases where removing noise from a photo is enough, computer vision can help with more corrupted images that require significant changes and detailed analysis. Corrupted parts of the image are often filled using generative models that evaluate the content of the photocast.

USE CASES

In addition to recovering images and videos, modern neural networks can reconstruct 3D scenes simply by scanning objects in a photo. Scene reconstruction is a game-changing computer vision paradigm used by archaeologists, forensic experts, environmental scientists, and many other professionals. Projects like RetrievalFuse are able to build panoramic 3D scenes from a single RGB image.

5. Human pose estimation

Pose estimation aims to simulate human visual capabilities, especially recognizing postures and gestures in images and videos. Some of the earliest examples of advanced human pose estimation appeared in big-budget movies such as Peter Jackson's The Lord of the Rings. As computing resources scale over time, pose estimation will come into play in many different products.

USE CASE

In security applications, pose estimation helps identify potential troublemakers by analyzing gait where facial recognition is not feasible. Computer vision can help detect shoplifting in real-time by analyzing body posture. 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 are alerted and can react quickly before the thief leaves the store.

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

• Rehabilitation measure analysis.
• Develop an AI-based fitness coaching app.
• Identify the position of the human body in space to improve augmented reality applications.
• Game character animation.
• Analysis of people’s activities in stores and shopping malls.

While pose estimation was once a huge computational challenge, innovations in cloud computing and hardware have made this technology accessible to more companies.

Everything is possible

Object detection, facial recognition, scene reconstruction, image restoration and human pose estimation are just a few different implementations of computer vision technology. Thanks to the power of next-generation AI, no matter what industry your business operates in, computer vision can provide a unique advantage that puts your company ahead of the competition. From reconstructing full-depth 3D models of crime scene photos to identifying defects in mass-produced products on factory lines, computer vision continues to change the way everyone does business.

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