We describe how to use Intel® System Studio 2015 to build the OpenCV* 3.0-based embedded application on Intel platforms. In this paper, we have considered a sample code that is part of OpenCV, how to use different components of Intel® System Studio to build OpenCV sample code.
Instructions to set up the environment of IPP on OpenCV on Windows* or Linux* Ubuntu*.
You can use LibRealSense and OpenCV* to stream RGB and depth data from your connected Intel® RealSense™ camera. This tutorial and code sample shows how to do this, based on the Ubuntu* operating system. In the end you will have a nice starting point where you use this code base to build upon to create your own LibRealSense / OpenCV applications.
This article shows you how you can use LibRealSense and OpenCV to stream RGB and depth data. In the end you will have a nice starting point where you use this code base to build upon to create your own LibRealSense / OpenCV applications.
This paper addresses how the Smart Video (SV) system architecture is increasing in complexity and evolving into new industries and use cases.
In support of computer vision development efforts, we've created a GitHub* repository of twelve computer vision code samples. These code samples are a good starting point for developers who wish to develop more robust computer vision and analytic solutions. We use the Retail, Digital Signage market in these examples but the technology can be used in a variety of different markets.
Inference of Caffe* and TensorFlow* Trained Models with Intel’s Deep Learning Deployment Toolkit Beta 2017R3Installing Deployment Toolkit
With the amount of continuously generated data on the rise, the cost to upload and store that data in the cloud is increasing. Data is being gathered faster than it is stored and immediate action is often required. Sending all the data to the cloud can result in latency and presents risks when internet connectivity is intermittent. Edge computing involves processing data locally for immediate...