In the modern world, tech giants are harnessing the power of AI-based image recognition technology to revolutionize various sectors. From healthcare to marketing, transportation to e-commerce, this technology is being used to classify photographs by locating certain objects within them. It has the potential to improve the quality of life of people around the world, from medical care to funding. Computers interpret each image as a raster or vector image; therefore, they cannot detect the difference between different sets of images.
This is why U-Net is believed to be superior to Mask R-CNN, especially in tasks as complex as medical image processing. To facilitate the use of these techniques, as well as to implement AI-based image processing capabilities in your product, you can use specific libraries and frameworks. APIs provide an easy way to perform image recognition by calling a cloud-based API service, such as Amazon Rekognition (AWS Cloud). This service uses AI image recognition technology to analyze images by detecting people, places and objects in those images and grouping content with similar characteristics.
Since human lives depend on the results of the work of this algorithm, each and every image frame it processes must be examined with precision in real time as soon as possible from a physical point of view. This platform provides an image processing (IPT) toolbox that includes multiple algorithms and workflow applications for AI-based image analysis, processing and visualization, as well as for the development of algorithms. In the field of deep image recognition, convolutional neural networks even surpass humans in tasks such as classifying objects into detailed categories, such as the particular breed of dog or the species of bird. This allows AI images to be processed in real time, since visual data is processed without downloading data (uploading data to the cloud), which allows for higher inference performance and the robustness needed for production systems. We develop artificial intelligence and deep learning solutions based on the latest research on image processing and on the use of frameworks such as Keras, TensorFlow and PyTorch. CNNs are widely used to implement AI in image processing and to solve problems such as signal processing, image classification, and image recognition.
The potential of AI-based image recognition technology is immense. It can be used for a variety of applications ranging from medical diagnosis to fraud detection. With its ability to process images in real time and its robustness for production systems, this technology has become an invaluable tool for businesses across industries.
