AI & Discount Knowing what discount to offer is always a tricky situation. You want to get more customers, but at the same time, you don’t want to lose out more on profit. Luckily, you have AI to help you decide the right discounts for your products and services. An efficient AI system can suggest the ideal discount rate and ensure that you are making more profit from it. It looks into specific features of each past deal that was lost or won. This is what makes AI so useful and efficient in discount management. Getting Ready For AI-Driven Pricing Price optimization led by AI requires retailers to follow five steps. This include: Prepare the required data Hire a technological provider or look for an in-house solution Select products for AI to suggest prices Educate and train managers Keep the system up and running AI is a very powerful and beneficial technology when used correctly. Retailers can gain a lot from implementing the power of AI in price and discount management.

 How fascinating would it be to extract hidden values from images and understand them even better! Computer vision is a subpart of artificial intelligence that provides AI and computer science enthusiasts the ability to modify and transforming images according to their imagination and creativity using algorithms.

Computer vision (CV) is defined as an interdisciplinary scientific field that deals with how computers can gain a high-level understanding from digital images or videos. It helps computers to understand the content and characteristics like shape, color, texture, and size of digital images and videos.

Following are some of the latest applications in computer vision.

General use cases-

• Creating a 3D model using 2D images – Using computer vision to make 3D models from 2D images is called photogrammetry. Several images taken at 360 degrees around the object are supplied to an algorithm that returns the 3D model. Photogrammetry is used in fields such as topographic mapping, architecture, engineering, manufacturing and geology.

 

• Human pose estimation – Pose estimation is the technique of understanding the pose of a person or an object from a digital photo or video. It is also sometimes referred to as the localization of human joints as it predicts the positions of a person’s joints in an image or video Pose estimation can be done in 2D as well as in 3D. Human pose estimation has many applications such as those in the field of augmented reality and motion capture. It is also used for training robots where instead of manually programming robots to follow trajectories, they are made to follow the trajectories of human pose skeletons.

 

• Digital Image Processing – Many real-world problems in computer vision require integrating a large number of images to create computer vision systems. When approaching such computer vision problems, processing one image in the context of some other might be required. GAN (Generative Adversarial Networks) is a technique that comes under the machine learning domain and uses training set data to generate new data. There are many applications of GAN in computer vision with some of them being generating realistic photographs, generating cartoon characters and emojis, and editing photographs.

Industry-Specific Use Cases

• Computer vision in healthcare and medicine- Computer vision has a wide range of applications in the medical sector.
  • Computer vision techniques are used in the healthcare sector for diagnoses like CT scans, X-rays, and MRIs. These technologies are used to convert images obtained on scans to 3D models for better understanding and detect anomalies like tumors and neurological illnesses. Images are extracted from scans and used to train models that detect anomalies.
  • Gaze tracking and eye-area analysis are also widely used applications of computer vision to detect cognitive impairments.

 

• Automotive industry– Tesla is one of the few automotive companies which uses Autopilot features in its cars. These self-driving vehicles have cameras attached to them that can record live footage and allow computer vision to create 3D visualizations. Using these 3D maps and visualizations, accidents can be prevented.

 

• Agriculture– There can be numerous ways to use computer vision techniques in agriculture for purposes like crop monitoring, livestock management, and forestry management. Cameras installed in drones click pictures of agricultural fields which can be used later to train computer vision models to enable them to do work like spraying adequate amounts of pesticides and hence monitor crop growth. Also, a well-trained drone can identify livestock, count, and monitor them. They can also be used to check the growth and health of trees in forests and take into account disruptive activities if any like deforestation.

Computer Vision in COVID times

• Development of social-distancing tools-One of the precautionary measures to prevent infection from coronavirus is social distancing. In order to ensure whether people in an area are following the social distancing norms, computer vision techniques are being used. One of these techniques is using object detection and tracking. Each person appearing in the video being recorded in real-time is detected using a bounding box. Further, the movement of the boxes is tracked and distances between two adjacent boxes are calculated. If there is any kind of violation detected, the boxes are consequently highlighted.

 

• In the medical sector – The most recent use of computer vision in the medical sector is identifying Covid-19 infections. A common symptom of computer vision is pneumonia. Analyzing chest X-rays can help in detecting Covid-19.

 

In a nutshell, it can be concluded that computer vision as a subfield of artificial intelligence has found its applications anywhere and everywhere in the present scenario, and in the near future, it will effectively harbinger AI technologies that are as human as us.