Vision AI: Tool that can Recognize Images and Videos

Talking about new technologies here is contradictory since many technologies have been part of our daily lives for years. Artificial intelligence has really come to make our lives easier. One such application is Vision AI. Do you want to know what Vision AI is? Keep reading!

What is Vision AI?

Vision AI uses artificial intelligence to process and analyze large amounts of images in real time. Through this tool the image identification and classification process is automated. Otherwise this would require a lot of time from a person due to the level of detail or high specialization.

Vision AI Features

Key features of the Vision AI platform include web detection, character recognition, logo detection, and color attribute detection. Other Vision AI features include:

Rest API

Vision AI features a RESTful API, an application programming interface that can be configured based on the limitations of the REST architecture. It also allows interaction with other RESTful web services.

Safe Search

Vision AI also includes the use of the Google Image Safe Search search engine. This acts as an automatic filter to eliminate offensive or inappropriate content. Vision AI includes AutoML Vision integration that trains custom, machine learning models in the cloud. This allows you to understand the images better.

The platform also allows users to easily upload images and train image models using highly intuitive guidance. The benefit of this integration is that the system optimizes model accuracy, latency, and size, while allowing users to more easily export to a variety of devices or cloud applications at the edge.

Vision API

Another computer vision element integrated with Vision AI is the Vision API, which serves to provide pre-trained and learned models with autonomous operation and robust performance. This integration helps the system to assign tags to images and perform classification processes into numerous predefined categories.

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How does Vision AI work?

Its job is mainly to mimic the behavior of how we use vision to understand our environment, allowing us to obtain valuable information in real time. Thus, as its simplified form and name suggest, the task is based on complex artificial neural networks.

After processing the images, they can provide a better understanding of the environment without receiving external information. This without the operator entering the system. In this way, computer vision can be programmed to recognize and understand images according to predefined patterns and, after recognition, determine the necessary actions (storage, classification, calculation, warning, etc.).

In fact, the computer obtains a database of images of a particular article or topic. It then identifies patterns in that image, shows what it sees, and creates a model of the element or theme under consideration. You can clearly see if the next image or video in your catalog falls into that category.

You can compare the way Computer Vision works to the way humans solve puzzles. In computer vision, a neural network examines and assembles the pixels that make up an image, identifying the parts, edges and possible combinations that make up the image.

One of the biggest strengths of computer vision today is machine learning (ML). This field of artificial intelligence has an accelerated ability to recognize patterns, correct errors and deliver results in complex and highly accelerated processes using thousands and thousands of data.

It can provide the computer with enough data about the context of a particular image. Finally, the algorithm ensures that the machine sees the data independently and learns to distinguish one image from another.

Areas in which this AI app is developed

Thanks to advances in this field, current artificial intelligence systems implement computer vision in areas such as:

• Pattern recognition: Recognize colors, silhouettes and shapes that repeat in images. Image Classification – Classify images as intended.

• Image Segmentation: Examines the different parts and components of an image.

• Identify common characteristics: Identify and group similar patterns in images.

• Facial Recognition: Identify both human and real faces.

Challenges in computer vision

The availability of ImageNet has made a huge difference in the growth and adoption of computer vision. It literally became the basis of the industry. But it also shaped technology in ways that have real-world implications today.

The falsification of algorithms and data is one of the central problems of AI in general, but its effects can be easily seen in some computer vision applications. For example, facial recognition technology is known to misidentify people of color, but its use in stores is growing.

This is also common among police officers and has led to protests and the implementation of ordinances in several cities and states in the United States.

Computer vision also presents some technical challenges. Limited by hardware, including cameras and sensors. Furthermore, computer vision systems are very complex in scale. And like all types of AI, it requires enormous amounts of computing power (which is expensive) and data.

And as the entire history of computer vision shows, good data that is representative, unbiased, and ethically collected is difficult to find, and incredibly tedious to label.

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Where is Vision AI used?

The areas where computer vision is currently used will not be covered in this short blog post, but we can highlight some applications in the following important areas as examples of its potential:

• Retail/Mass: Track customer journeys, calculate total time spent on each product. Profile customers who are likely or unlikely to buy and more.

• Due to the detailed monitoring of commercial activities: Activities related to fraud and theft.

• Pharmacy/Wellness: Individualize the treatment (avoid overproduction or contraindications), specify manufacturing processes, etc. for each case. Develop predictive models that improve customer insights more effectively.

• Travel/Tourism: Increase revenue efficiency by predicting trends and identifying specific products and services to offer each customer based on behavioral characteristics and habits.

• Energy/Utilities: Analyze data and images to anticipate demand, reduce environmental impact and energy consumption, prevent fraud, and personalize service delivery.

• Transportation/Logistics: Use RFID tracking and monitoring technology on mobile cameras without expensive infrastructure.

• Marketing: Base and processing of information on the real behavior of users, detailed knowledge of consumption habits through segmentation and analysis of customer profiles at a level of information higher than that provided by web analytics or censuses.

• Education: Provides high-quality information on topics and areas that simulate real-life situations and make learning more accessible and effective.