Surface Damage Characterization Using Image Analysis, Human Visual Rankings and Customized Algorithms ( US 2018 146 )
Created:
| Last Modified:
Level: Intermediate
Evan Bittner, Statistical Engineer, Corning
Christine Cecala, Senior Research Scientist, Corning
David Brockway, Senior Statistical Engineer and Distinguished Associate, Corning
Casey Volino, Senior Engineering Associate, Corning
Eric L. Null, Senior Chemical Measurement Scientist, Corning
Durability of cover glass on displays is an important attribute for consumer devices. To assess cover glass resistance to surface damage, quantitative metrics are needed that can discriminate between different samples in laboratory testing while maintaining a relationship with human visual rankings to accurately simulate user experience. To move from subjective, highly variable rankings to robust quantitative metrics, the VIRTUAL Eye measurement system was developed. Key features include two edge lights to illuminate samples and an overhead camera to capture images of surface damage. With images of surface damage on hand, JMP is used to drive the data processing, quantitative analysis and user feedback. Usable pixel intensity data is extracted from images; in-house algorithms were created and developed to extract the metrics from that data. Established platforms such as Graph Builder and Quality and Process pushed the analysis of data and metric creation forward, while scripting provided power, flexibility and efficiency throughout the development process. The tools in JMP helped provide a path to quantifying the visual perception of surface damage.
