Machine vision is used in a variety of industrial processes, such as material inspection, object recognition, pattern recognition, electronic component analysis, along with the recognition of signatures, optical characters, and currency.
Digital image sensors work based on the Photoelectric effect. When the image sensor is exposed to light (photons), an equivalent charge is produced by the image sensor. It is then converted to image data.
Robotic sight and machine vision systems can help our automata pick up on visual cues that we humans would probably miss or ignore, and they can deliver considerable value to companies that adopt them. Here's a look at where the technology has been and where it's headed.
The conditions placed on original equipment manufacturers and suppliers are immense in order to ensure high quality and continuous improvement, waste reduction and error minimization.
The traditional vision architecture is changing, with an evolution from cameras and sensors to networked and smart-enabled, compact embedded devices with the processing power required for real-time analysis.
For manufacturers, the reliability of the quality control of industrial parts of all kinds is crucial, because defective parts due to non-conformity have serious effects on production performance.
High-speed CXP 2.0 is ideal for AV and ADAS (Advanced Driver Assist Systems) connectivity applications. Camera images from multiple sources around the vehicle, along with data from sensors capturing object shape, speed and distance.
Autonomous Machine Vision (AMV) offers a revolutionary approach to QA, allowing manufacturers to have access to less expensive and user-friendly technology that does not require the intervention of any external expert.
Acquisition of Proven Leader in Mission-Critical Robotics Systems for $385M Will Provide FLIR Entry Into Attractive Unmanned Ground Vehicles Market for Military, Public Safety, and Critical Infrastructure
Autonomous robot showcases contextual awareness and next-generation natural UI
There are numerous approaches to robot guidance using machine vision techniques, such as stereo vision and photogrammetry, time of flight, structured light, light coding, and laser triangulation.
Structured Light imaging is commonly used for machine vision because it can yield high resolution results. Some of the methods can be used effectively in moderate and high-speed applications.
As the plant floor has become more digitally connected, the relationship between robots and machine vision has merged into a single, seamless platform, setting the stage for a new generation of more responsive vision-driven robotic systems.
CCD (charge coupled device) and CMOS (complementary metal oxide semiconductor) image sensors are two different technologies for capturing images digitally. Each has unique strengths and weaknesses giving advantages in different applications.
"The startups that have made it to the final competition are indicative of the levels of innovation in the machine vision and imaging industries," according to Jeff Burnstein, President, AIA. "I don't envy the job in front of our judges having to select only one winner."
Records 16 to 30 of 103
FAULHABER MICROMO launches the new MC3/MCS motion control family. The new high performance, intelligent controllers are optimized for use with FAULHABER motors, offer electronics for simple operation with state-of-the-art interfaces for multi-axis applications, and provide a motion control system solution with the most compact integration into industrial grade housing.