Versions Compared

Old Version 17

changes.mady.by.user Beate Huber

Saved on

compared with

New Version Current

changes.mady.by.user NET GmbH

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

Excerpt
Table of Contents

Video Session 1: Getting started with iam and Smart App Examples

Confluence youtube macro video
fullscreenConfluenceButton
privacyEnhancedtrue
width800
videoIdhttps://youtuwww.youtube.be/U2WP8hcGfN8com/watch?v=FdP2iPrMXC8
height450

related content

content

0:00 Introduction: - GigE Vision - SDK - SynView

3:32 Live demo 1: Overview

  • 6:25 Initial connection with camera as GigE Vision device via GigE Vision Server

  • 9:20 Connection with camera via SSH/X11/remote desktop

  • 1:10 Running SynView on the PC and on the camera

  • 13:25 Summary and lessons learned

14:15 Live demo 2: Overview

  • 15:02 Running a demo application with OpenCV on a PC

  • 22:53 Compiling and running a demo application in OpenCV on the camera

Video Session 2: The NET Open Camera Concept

Infocoming soon…

Confluence youtube macro video
fullscreenConfluenceButton
width800
videoIdhttps://youtu.be/HkhEZL4qBvU?feature=shared
height450

related content

content

Part 1: Introduction to the Open Camera Concept
00:42 CORSIGHT: Smart Camera with software and hardware functions
01:39 iam: Smart Camera with open platform concept

Part 2: Hardware acceleration for iam
04:07 Hardware acceleration: optimizing effects
06:01 Hardware acceleration: kernel types Cross Compiling for iam
10:32 Cross compiling introduction
11:53 Xilinx Vitis IDE

Part 3: Xilinx Vitis IDE - Example Application
14:48 OpenCV application example w/o hardware acceleration on a camera
17:54 Optimization of the OpenCV application example

​

Video Session 3: Machine Learning with iam

Infocoming soon…

Confluence youtube macro video
fullscreenConfluenceButton
privacyEnhancedtrue
width800
videoIdhttps://www.youtube.com/watch?v=6FJaaO-Pizw
height450

related content

Contents

00:00 Introduction to Machine Learning image processing applications

03:05 Basic Machine Learning applications: Classification, segmentation, object detection

05:40 Implementation of Machine Learning to iam

08:24 Network Development Flow

10:16 Required software environment

11:59 Application example: Classification of fruits and vegetables

13:38 Network Training

22:41 Network Optimization and Transformation

32:30 Running Application on iam

🔍 References

🔗 related content

Quick Start

Software

👥 contact NET

Insert excerpt
iam:MasterContent: Contact NET
iam:MasterContent: Contact NET
nopaneltrue