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5 Ways Computer Vision Helps Solve Your Business Challenges


computer vision business challenges
Description: © IoT For All

Self-propelled cars, traffic sign recognition, face recognition, and self-checkout. What unites all of these progressive solutions is computer vision. Computer vision allows computers to extract information from raw images and opens up many opportunities for more effective business digitalization. Let’s take a look at how the computer outlook has disrupted various industries and what unique benefits it has brought to help owners solve key business challenges.

#1: Check the thing

Traditional computer vision implementations use in-depth analysis of inputs and outputs. The typical case flow of the old-school CV relies on image processing techniques such as edge detection to identify and mark objects in an image.

The advent of in-depth computer science learning architecture has led to a major shift from classical CV techniques such as meaning-based structures to neural network analysis. image -driven AI, allowing almost complete automation of the acquisition and classification of image data. In simpler terms, artificial intelligence takes programming out of the image in favor of a less controlled approach in which the computer interprets input data and trains itself to recognize the content of images.

Use Cases

When AI enters fields such as medical imaging, the computer uses optimal pattern recognition to identify subtle elements in raw images, such as whether cancer cells are present or not in the small amount of an X-ray or MRI. Despite the fact that human interpretation and skill are still needed to diagnose machine cuts, an additional layer of lightning -fast analysis can help increase human intelligence and save lives.

While self -driving cars are on the road across the United States and many other countries, the CV field is seeing rapid growth. Autonomous cars would not exist without computer vision. Because the car’s onboard computer needs to make immediate decisions about potential roadblocks, it relies on a more optimized set of CV-based technologies.

It is important to note that in areas such as medicine, security, manufacturing, etc., transparency in how to build an AI-powered system is of paramount importance. This is where the explicit AI comes into play. This technique allows for interpreting the knowledge of the system in a way that people can understand and demonstrates the reliability of a particular decision made by an AI algorithm.

Use computer vision to address the following business challenges:

  • Public safety (vehicle identification, weapon type identification, search for suspicious items, etc.)
  • Automation of sales and inventory management (identification of low stock or not placed items on shelves, identification of empty shelves, performance of quality control, product identification for self checkout, etc.)
  • Eliminate human error and prevent 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 recognize and extract letters, numbers, and other characters from input images.

Use Cases

Google Lens uses OCR to allow customers to translate foreign languages ​​from photos and extract text from images or search on Google. OCR technology also makes it simple to digitize legacy media, extracting text from scans of newspapers, magazines, and books. In the past, universities have struggled to digitize less obscure documents such as Tibetan Buddhist religious texts, but modern OCR technology makes it easier to extract text from non-standard language documents.

Financial institutions use OCR for improving the quality of life for customers, such as allowing customers to retrieve their International Bank Account Number (IBAN) from documents or scan images of check so you no longer have to go to the bank to deposit. . Some apps can scan a debit or credit card to input payment details, so you don’t have to be bored typing all your payment information into the checkout window.

Governments often use OCR to reduce processing time at national borders or to identify and register documents. The machine -readable zone of modern passports and driver’s licenses is compatible with OCR systems in government and commercial settings.

#3: Face Recognition

Like object recognition, face recognition aims to identify parts of a person’s face in an image using computer vision. Classical computer vision techniques use “Haar-like shapes” to calculate the features between facial features, but modern face recognition implementations rely on artificial intelligence to the same way AI is used for object recognition.

Use Cases

Face recognition technology is important in security applications because it helps prevent mobile and web application vulnerabilities. Countless Apple iPhone users have relied on Apple’s Face ID technology for biometric authentication to unlock their phones.

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

Face recognition can also help you with the following tasks:

  • Security and access control
  • Proof of identity
  • Employee tracking
  • Patient screening procedures in health care
  • Identify and track down criminals

Next-generation facial recognition software can even look at postures, movements, and facial expressions to determine if a customer may be cheating at a casino. Gait analysis combined with the same security software can also help detect criminals based on the unique pattern of their steps and steps, as many criminals hide from face recognition by to wear masks.

#4: Image Restoration and Scene Reconstruction

Computer vision technology also makes it possible to restore severe damage to archival footage and images that can be a critical business technique. Unlike simple cases where it is enough to extract noise from a photograph, computer vision can help with more damaged images that require serious editing and detailed analysis. Damaged parts of the image are usually filled in using generative models that evaluate what is transmitted in the photograph.

Use Cases

In addition to restoring images and video, modern neural networks can recreate 3D scenes simply by scanning objects in a photograph. Archaeologists, forensic specialists, environmental scientists, and many other professionals are using Scene Reconstruction, a game that is changing the computer vision paradigm. Projects like RetrievalFuse are able to create panoptic 3D scenes from an RGB image.

#5: Estimating Human Position

Pose estimation aims to mimic a person’s visual abilities, particularly in recognizing poses and movements in images and videos. Some of the oldest examples of advanced Human Pose Estimation show large budget figures such as The Lord of the rings by Peter Jackson. As computational resources evolve over time, pose estimation can be found in many different products.

Use Cases

In security applications, Pose Estimation helps identify potential disruptors by analyzing their gait where Facial Recognition is not possible. Computer vision can help identify store theft in real time by analyzing bodily movements. The system can distinguish between normal shopping behavior and suspicious behavior such as taking something and hiding it in a pocket or clothing. If suspicious behavior is detected, managers can get alerts and can quickly respond to the situation before the thief leaves the store.

Here are some ways to use estimating the pose of your business:

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

Although estimating the pose used to be a major computational challenge, innovations in cloud computing and hardware are putting the technology within reach of many companies.

Heaven is the Limit

Object detection, face recognition, landscape reconstruction, image restoration, and human pose estimation are just a few of the various implementations of computer vision technology. Given the powerful power of next-generation AI, there is a good chance 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 photographs to identifying imperfections in products made in most of the factory line, the pan the computer system continues to change the way everyone does business.





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