Natural Scene Derived Camera Edge Spatial Frequency Response for Autonomous Vision Systems
| Autor: | Alexandra Psarrou, Robin B. Jenkin, Sophie Triantaphillidou, Oliver van Zwanenberg |
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| Rok vydání: | 2021 |
| Předmět: |
Measure (data warehouse)
Computer science business.industry Linear system System quality SFR MTF modulation transfer function Signal quality convolutional neural networks Deep neural networks spatial frequency response image quality Computer vision Enhanced Data Rates for GSM Evolution Artificial intelligence business Optical resolution |
| DOI: | 10.34737/v644w |
| Popis: | The edge Spatial Frequency Response (eSFR) is an established measure for camera system quality performance, traditionally measured under laboratory conditions. With the increasing use of Deep Neural Networks (DNNs) in autonomous vision systems, the input signal quality becomes crucial for optimal operation. This paper proposes a method to estimate the system eSFR (sys-SFR) from pictorial natural scene derived SFRs (NS-SFRs) as previously presented, laying the foundation for adapting the traditional method to a real-time measure. In this study, the NS-SFR input parameter variations are first investigated to establish suitable ranges that give a stable estimate. Using the NS-SFR framework with the established parameter ranges, the system eSFR, as per ISO 12233, is estimated. Initial validation of results is obtained from implementing the measuring framework with images from a linear and a non-linear camera system. For the linear system, results closely approximate the ISO 12233 eSFR measurement. Non-linear system measurements exhibit scene dependant characteristics expected from edge-based methods. The requirements to implement this method in real-time for autonomous systems are then discussed. |
| Databáze: | OpenAIRE |
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