Machine Vision

Introduction

Performance, accuracy and energy efficiency are critical parameters for machine vision solutions at the edge. Edge computing solutions facilitate data processing near the source of data generation and serve as a decentralized extension of the cloud or data center networks. This eases the integration of machine vision with lower latency and reduces bandwidth by filtering the relevant data at the edge.

Machine Vision surrounds all industrial and non-industrial application in which a combination of hardware and software provides operational guidance to devices in the execution of their functions, based on the capture and processing of images.

Machine Vision systems rely on digital sensors protected inside industrial cameras with specialized optics to acquire images, so that computer hardware and software can process, analyze and measure various characteristics for decision making.

Machine Vision improves quality and productivity, while driving down manufacturing costs!
Machine Vision Process: Image Material IN / Useful Data OUT

Jan Venema – CTO AimValley B.V.

Benchmarking

Using Accelerated Edge Computing (AEC) benchmarking, profiling and tuning tools we quickly identify performance bottlenecks in your existing applications. And migrate the key latency or bandwidth critical sections for hardware assisted off-load to an FPGA accelerator card. Processsing and throughput improvements by a factor of 5 or more are possible, depending on the algorithm; and power consumption can be reduced significantly when compared with graphics cards.

Applications

Automated Optical Inspection
Motion Control Systems
Process Automation
Ultra Low Latency Cameras
Vision Guided Robotics
Quality Control
Product Tracing, Scanning & Identification
Medical Imaging

Benefits

Ultra Low Latency
Energy Savings
Efficient and compact solutions
Flexible and future proof technology
Knowledge & KnowHow of using FPGA

Machine Vision Videos

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