Artificial intelligence (AI) and machine learning (ML) are being progressively used across different sectors including healthcare. One of the AI-powered tools is computer vision (CV), the ability to recognize, interpret, and process visual data. Thus, potential applications of computer vision in the medical field are multifold, from image processing and predictive analysis to automated health records. All this enables improving the quality of delivered medical services and the healthcare administration system.
Deep learning (DL) and neural networks are extremely widespread in different computer vision (CV) applications. Indeed, many typical problems (like object recognition or semantic segmentation) are effectively solved by the convolutional neural networks (CNNs). In this article, we are going to discuss how to utilize CNNs on embedded devices. Neural networks today are ubiquitous. In particular, it is hard to imagine computer vision without them.
Computer vision (CV) and machine learning (ML) algorithms solve a tremendous amount of problems. However many businesses often do not understand what hardware to choose for running your favorite neural net or some advanced image and video processing pipelines. With this blog post, we start a series of articles about embedded vision and specific practical things you need to know before making your choice.
Our client’s goal was to enhance various printed media (magazines, posters, banners, etc.) with interactive experience using augmented reality. With AR, certain areas on the reading materials can be overlayed with digital information of a different kind: from videos, images, and 3D models to weather information and buttons that bring additional functionality, etc.
The task of automatic document analysis and recognition is very common in everyday life. Basically, every time when a user needs to automatically parse and recognize some content from a picture captured with a mobile phone/tablet or a scanned document – for example, text, tables, links, etc., automatic document recognition and text analysis come to the stage.
Fiducial markers are widely used in various applications like robot navigation, logistics, augmented reality. Fig. 1. Applications of fiducial markers Advantages are obvious High contrast Simple code generation Resistance to extremal angles However, when we deal with a large number of markers, real-time recognition becomes challenging, especially on embedded devices with low power CPUs on-board.
Currently, the number of cars in the world is well over 1 billion. It is no wonder that one of the most common computer vision tasks is the effective control of these vehicles through automatic number plate recognition (ANPR) systems.
Our task was to develop the algorithm for the automatic road detection in radar images. The challenge was that the radar images are a bit different from the optical ones. In particular, in the case of synthetic aperture radar (SAR), the image formation process is accomplished via coherent processing of the received signals backscattered from the Earth surface. As a result, the multiplicative speckle noise appears in the SAR images.