Post-Processing Data From RGB-IR Sensors

Tools for processing simulation results from a camera sensor or ODX import can extract meaningful insights and improve the quality of the data presented, empowering decision-making in manufacturing processes. Systems currently on the market are capable of processing spectral irradiance or generating an exposure map: outputs that accurately represent how a scene will be captured under real-world illumination conditions while considering the optical and sensor properties of the camera.

By integrating sensor data according to the EMVA-1288 standard (the standard for measurement and presentation of specifications for machine vision sensors and cameras) and applying advanced post-processing, such a system can generate detailed electronic maps, noise maps, raw images, and final/developed images representing realistic full-camera perception. These features are valuable in applications such as surveillance and security cameras, biometric scanning, automotive detection systems, healthcare devices, and industrial automation, where the RGB part of the sensor will work well in daylight conditions, while the IR pixels of the sensor function well in low-light or night settings.

About the presenter
Noah Hamstra graduated from the University of Arizona in 2020 with a bachelor's degree in optical sciences and engineering. After a brief time working with Reformed University Fellowship, a Christian ministry on campus at the University of Arizona, he joined the product support engineering team at Edmund Optics where he supported customers from academic to hi-tech to defense. Currently, he works for Ansys on their application engineering team supporting a system level optical simulation software called Speos. He is passionate about optical and optomechanical design, simulation, stray light, and imaging systems. Hamstra loves to call Tucson home and enjoys running, hiking, 3D printing, and music in his free time.