Use cases of AI-based image recognition

The way image recognition works, typically, involves the creation of a neural network that processes the individual pixels of an image. Researchers feed these networks as many pre-labelled images as they can, in order to “teach” them how to recognize similar images. This (currently) four part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works.

With the application of Artificial Intelligence across numerous industry sectors, such as gaming, natural language procession, or bioinformatics, image recognition is also taken to an all new level by AI. This may be null, where the output of the convolution will be at its original size, or zero pad, which concerns where a border is added and filled with 0s. The preprocessing necessary in a CNN is much smaller compared with other classification techniques. In other words, image recognition is a broad category of technology that encompasses object recognition as well as other forms of visual data analysis. Object recognition is a more specific technology that focuses on identifying and classifying objects within images.

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In this blog, we take a look at the evolution of the technology to date. Subsequently, we will go deeper into which concrete business cases are now within reach with the current technology. And finally, we take a look at how image recognition use cases can be built within the Trendskout AI software platform. Google, Facebook, Microsoft, Apple and Pinterest are among the many companies investing significant resources and research into image recognition and related applications. Privacy concerns over image recognition and similar technologies are controversial, as these companies can pull a large volume of data from user photos uploaded to their social media platforms.

The key points detected are indexed by the part IDs (for example, BodyPart.LEFT_ELBOW ), with a confidence score between 0.0 and 1.0. The confidence score indicates the probability that a key joint is in a particular position. You can find all the details and documentation use ImageAI for training custom artificial intelligence models, as well as other computer vision features contained in ImageAI on the official GitHub repository. One of our latest projects is a solution for insurance business that helps to detect car damage after it got into a crash. We all know that telehealth apps already shape the future of medicine. But what if we tell you that image recognition algorithms can contribute drastically to the further improvements of the healthcare industry.

Leveraging Transfer Learning for Efficient Image Recognition

In the example used here, this was a particular zone where pedestrians had to be detected. In quality control or inspection applications in production environments, this is often a zone located on the path of a product, more specifically a certain part of the conveyor belt. A user-friendly cropping function was therefore built in to select certain zones. Everyone has heard about terms such as image recognition, image recognition and computer vision. However, the first attempts to build such systems date back to the middle of the last century when the foundations for the high-tech applications we know today were laid.

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