🦉 Introduction
| Excerpt-include | ||||||
|---|---|---|---|---|---|---|
|
| Excerpt |
|---|
iam provide 64-bit processor scalability while combining real-time control with soft and hard engines for graphics and video. In this section we discribe |
possibilities to build your own younique smart vison system by using the provided |
infrastructure of iam cameras. There are three main topics discussed below. The iam basic system describes the out-of-the-box system. With the provided tools it is easy to configure the right camera setup. The next section gives an introduction to the iam customization possibilities. The iam ML version is the best starting point for artificial intelligence application and provides a Deep Learning Processing Unit in the FPGA section of the system. |
| Confluence youtube macro video | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
iam Basic System
The iam basic system is sketched below. In the out-of-the-box state the camera performs as an common GigE-Vision device and makes it easy to install the system and finding the right camera setup.
How to start with the iam camera system is described in the Quick Start section. The section SynView gives us more detailed discription of NETs Software Development Kit (SDK).
iam Customization Possibilities
iam provide 64-bit processor scalability while combining real-time control with soft and hard engines for graphics and video. With the NET SDK and GigE - Vision toolboxes sketched below customers can start from a comfortable starting point to build their unique vision system with iam. The open system architecture of iam enables customers to use both CPU and FPGA processing resources in their application.
In the chapters Applications we provide different example projects to transform iam into your own smart vision system while chapter Third Party Libraries provides step by step guidelines and example codes for the most widely-used comerial commercial vision libraries such as https://net-iam.atlassian.net/wiki/spaces/iam/pages/51511463/Halcon HALCON , MIL (Matrox Imaging Library) and /wiki/spaces/iam/pages/844627987.
In section https://net-iam.atlassian.net/wiki/spaces/iam/pages/96501812/Synview+Smart+App+Example+using+ SynView Smart App Example using iAMGigEServer you will find a simple software example application while in section https://net-iam.atlassian.net/wiki/pages/resumedraft.action?draftId=79986879 NET Open Camera Concept (OCC) for iam we briefly described how to optimize the application code by using hardware acceleration with more advanced examples.
iam ML - Machine Learning Ready
iam ML Version is ideal as a platform for Machine Learning tasks. The integrated hardware acceleration efficiently supports common neural networks framework such as Caffe, TensorFlow and MXNet. This means that users get a smart vision system that contributes decentrally to the application solution. iam enables them to develop precisely tailored solutions for their vision-based processes from an toplevel perspective.
Section iam ML - Machine Learning ready provides step by step guidelines and example codes from training- over deploying- until performing convolutional neural network processing with iam.
📚 Content
| Child pages (Children Display) | ||||||||
|---|---|---|---|---|---|---|---|---|
|
🔗 related content
🌍 external media
| Insert excerpt | ||||||
|---|---|---|---|---|---|---|
|
👥 contact NET
| Insert excerpt | ||||||
|---|---|---|---|---|---|---|
|