Ryan Johnson, lead engineer at Twisthink, presents the “Optimize Performance: Start Your Algorithm Development With the Imaging Subsystem” tutorial at the May 2018 Embedded Vision Summit.
Image sensor and algorithm performance are rapidly increasing, and software and hardware development tools are making embedded vision systems easier to develop. Even with these advancements, optimizing vision-based detection systems can be difficult. To optimize performance, it’s important to understand the imaging subsystem and its impact on image quality and the detection algorithm. Whether performance improvement involves tuning an imaging subsystem parameter or increasing algorithm capability, it is the designer’s responsibility to navigate these relationships and trade-offs.
This presentation describes a design approach that allows the designer to iteratively adjust imaging subsystem performance while increasing the fidelity of the detection algorithm. Viewers will gain an understanding of several high-impact imaging subsystem noise sources, methods for evaluation and ways to determine requirements driven by the detection algorithm. Viewers will also learn how datasets enable evaluation of non-obvious noise sources and system performance.
